Alright, uh, good afternoon. Uh, welcome to the session. Uh, my name is Mokchada. Today, I will talk, be talking about FEDLIS, uh, Secure and Scalable Serverless Federated Learning. Uh, a bit about me, I'm finally a PhD candidate at the Chair of Computer Architecture and Pile Systems, uh, at the Technical University of Munich. I'm working mostly on solving various challenges in the domain of serverless computing and I've experienced in the domains of cloud computing, high performance computing, pile computing and systems for ML. So, uh, this presentation, uh, is structured as follows. Uh, first, I will give a brief introduction about serverless computing and federated learning followed by why this makes sense. Uh, then introduce FEDLIS and how FEDLIS addresses the, uh, straggler problem in federated learning. Some, uh, experimental results which we did with the system and finally, uh, what we are currently working on. Alright, so if you look at a brief history of the cloud, uh, the x-axis denotes, uh, focus on application or business logic and the y-axis denotes the virtualization stack abstraction. So, uh, in the beginning, uh, there were traditional IT where the unit of scale was physical servers with the deployment model being bare metal servers. Uh, these usually, uh, lasted for years and could be deployed in hours or days. Uh, with the introduction of virtualization, uh, VMs became the unit of scale and the deployment model as, uh, infrastructure as a service. Uh, VMs get, get deployed in minutes and live for weeks. Uh, further, uh, with, uh, introduction of OS level virtualization, containers became the unit of scale and deployment model as, uh, platform as a service. Uh, containers can be deployed in seconds and live for hours or minutes. Uh, so all these, uh, different deployment models are essentially server-based, uh, but what I mean by that is that the user or the application developer has to configure certain back-end server configuration parameters. So, uh, with the introduction of AWS Lambda in 2014, uh, uh, this new concept of functional service was, uh, introduced. So here, uh, the infrastructure management is completely handled, uh, by the cloud service provider and the application is essentially decomposed into fine-grained functions. And these functions became the unit of scale and they can be deployed in milliseconds and essentially, uh, live for seconds. So, uh, some prominent examples of service computing platforms today, uh, are, uh, commercial offerings such as AWS Lambda, Google Cloud Functions, Azure Functions, uh, IBM Cloud Functions, uh, open source alternatives such as, uh, OpenWisk, uh, OpenFast, and, uh, Knative. Right? So how did these platforms actually work? So, uh, Functional Service is an event-driven paradigm, uh, where functions are invoked, uh, on events such as, uh, HTTP requests or GRPC requests. Right? So, uh, on the occurrence of these events, uh, the Functional Service platform, uh, is responsible for providing resources to these functions and its isolation in ephemeral, uh, stateless containers called, uh, function instances. So on the cre- on- on function invocation, uh, if the service computing platform first checks, uh, if, uh, function ex- function instance exists or not. So if it does not exist, uh, the function, uh, the service computing platform creates a new, uh, function instance. Uh, this process is essentially what is called a call start. And, uh, this, uh, created, uh, function instance processes the event and then, uh, returns the response, uh, to the user. So the Functional Service platform or the Service Computing platform can create concurrent function instances, uh, to handle multiple events or requests on demand. Uh, when the number of requests decreases, uh, the fast platform can automatically scale- scale down the number of active function instances to zero. So, uh, to more- better understand what the call start process is. So you- essentially what happens when you have an event, you download the code, you start a new instance, and then bootstrap the function runtime, uh, and then execute the function handler, right? So this whole process, until bootstrapping the runtime, is what is called a call start. And if there's an already an active function instance, uh, which can handle the event, then, uh, only the handler method is executed, that is what is called a want start, right? So, uh, this part is essentially what is, uh, responsibility of the platform to optimize. All right. So, uh, moving on to, uh, federated learning, uh, so what it is, uh, it is essentially a distributed, uh, learning paradigm that enables the collaborative learning of MN models among a group of participants or devices or clients without sharing the data. So, uh, in contrast to traditional cloud-centric, uh, approach for deep learning, uh, which requires, uh, training data to be collected and processed at a central server, uh, in federated learning, the data never leaves the devices. Uh, instead the participants only exchange, uh, updated model parameters. So it tries to solve the fundamental privacy problems in, uh, distributed learning. So how does it work? So an FL system, uh, has two main components, uh, central server, uh, and a, and participating clients. The central server, uh, is responsible for coordinating the training process and contains the latest global model. Uh, the traditional FL training is done in synchronous rounds, and in each round a subset of clients participate in the training. So at the start of a round, uh, the central server sends the latest global model to a subset of devices, uh, to train and wait for the responses from those clients or for a pre-configured, uh, timeout. So when these, uh, devices receive the global model, they start training locally. After they finish, uh, they push the updates back to the central server. Uh, upon, uh, receiving the updates or when a timeout occurs, the server constructs, uh, a new global model by aggregating the updates, uh, from the clients. So this process is repeated until a desired accuracy is reached. So clients in federated learning can be mobile devices, edge devices, institutions operating their own data centers, or virtual machines, uh, managed by infrastructure service providers. So based on the scale of federation, uh, FL can be divided into two categories. So one is cross-device, uh, where the participating clients are mostly mobile and edge devices with limited compute capabilities. Another one is cross silo, uh, where organized, where clients are essentially organizations with, uh, sufficient, sufficient compute capabilities. So, uh, why, uh, bring serverless computing, uh, to federated learning? Doesn't make sense. So if you look at different challenges in FL, uh, right, uh, so since FL is traditionally a synchronous process, it turns out that, uh, clients that finish with their training, uh, often spend a lot of time waiting for others to finish until a new round begins. So this leads to unnecessary cost or wastage of resources. Uh, most clients have heterogeneous hardware. Uh, this often leads to, uh, resource over provisioning and cumbersome infrastructure management for the data holders. Uh, scalability, so at the end of each round, lots of clients report back their results at the same time suddenly. So FAS on the other hand, uh, fundamentally aims to solve many of these problems. So it provides rapid scalability, uh, during request births, automatic scaling to zero when resources are unused, and attractive pricing and development model, and finally, uh, ease of use. Right? So introducing FLLIS, uh, so it is a research project, uh, boosted up by me and one of my colleagues at TUM, uh, at the end of, uh, 2021. So what is it? Uh, it's basically a system and framework for federated learning on a heterogeneous fabric of functional service platform. What it means is that it can simultaneously work with clients in the cloud, edge devices, or on, on premise data centers. Uh, it provides essential features for security. Uh, it supports, uh, uh, training using differential privacy, uh, has a modular design and can be easily extended. Uh, it currently supports, uh, seven functional service platforms out of the box. Uh, supports multiple FL training strategies such as FedAbra, FedProc, Staffold, uh, supports, uh, training of arbitrary DNN models using TensorFlow and, uh, offers various performance optimizations, uh, to fix the shortcoming of serverless functions. Right? So let's look at the system architecture of FedLIS. So this, it can be grouped into several components. Uh, clients are essentially serverless functions, uh, which get their data from an S3 bucket or a mountain network drive. Uh, the controller is a stateful process, uh, responsible for, uh, managing and monitoring the entire training lifecycle. Uh, it includes the strategy manager, uh, and, uh, mocking system, uh, to simulate the platform as a whole on a single machine. So for debugging, clients and aggregator processes, uh, can run on a single machine without the need to deploy, uh, the clients on actual fast platforms. So the developer only needs to specify the mock flag while running the controller, and the rest is handled internal, internally by the mock invoker. Right? So the parameter server, uh, is MongoDB, which we chose because of its reliability, replication support, and creation of RBAC rules. And the aggregator functions are responsible for, uh, updating the model weights and computing, uh, the accuracy of the train models. So the security features of FedLIS can be divided into four aspects, uh, function ownership, uh, authentication authorization, uh, parameter server access, and, uh, general security features. So, uh, data holders are responsible, uh, for their own functions. Uh, so this enables, uh, complete trust through, uh, full control and flexibility. Uh, all participants and requests are authenticated. So only the trusted server, uh, can invoke, uh, client functions, and only identified and improved functions can join the heterogeneous fabric. And we support custom identity providers such as SAML 2.0 and OR 2.0. Uh, so the mechanism, uh, for this should be, uh, cloud agnostic, uh, agnostic to any fast platform and should require, uh, no manual intervention. So for this, uh, we make use of, uh, AWS Cognito, uh, which essentially uses, uh, JSON, uh, web tokens. So basically all requests to clients contain tokens signed by Cognito, and then clients can easily check if the token was actually signed by the Cognito user pool and sent by the FL server. We follow best practices for security and counter most of the vulnerabilities listed, uh, by the open web application, uh, system project with our system design, for example, isolating authentic, and authentication authorization by using, uh, separate functions. So, uh, a major problem in FL is privacy. Uh, so the, the main idea is that the model parameters can leak information about the training data. So there are essentially two types of attacks, which are called as membership inference attacks and more model inversion attacks. So there are different ways to prevent this. Uh, for example, encrypting the model parameters, uh, using techniques such as, uh, secure multiparty computing or homomorphic encryption, uh, using differential privacy, uh, but in most cases, uh, hybrid approach is often used. So encryption, uh, is problematic for fast because of huge computational overhead and complex, uh, inter-function communication. Uh, DP, uh, on the other hand, is well suited, uh, where the clients can just add noise to the parameters before uploading them onto the parameter server. So FLS by default implements local differential privacy, uh, which is a form of record level privacy in which the client functions add Gaussian noise to the parameters before uploading them to the parameter server. Right. Uh, so the core base, uh, of, uh, system is entirely written Python 3 with support for TensorFlow 2 plus and Keras. Uh, so it also incorporates in-built performance optimizations such as, uh, streaming aggregation, so which eventually performs, uh, a running average aggregation of the updated client model parameters, uh, so that the aggregator does not have to load all the, uh, model updates in memory at a single point. Uh, we also have this LRU cache, uh, for the normal global namespace due to the ephemeral, uh, stateless nature of the fast functions and we also implemented federated evaluation which enables, uh, like client side evaluation of global models so that you get better, fair accuracy results. So, uh, addressing one of the bigger problems in, uh, federated learning which is stragglers which are essentially, uh, slow clients. So, uh, stragglers can occur due to, uh, various reasons in federated learning. So, for example, one of the reasons can be system heterogeneity and some clients can have low computer sources. Uh, and since federated in FL, uh, most clients have non-independent and, uh, non-identical data distributions. Uh, some clients can have more data samples as compared to the other, uh, right? So, this can lead to statistical heterogeneity. Uh, they can be networking issues or complete failures. Uh, they can also be, uh, cold starts. So, if you look at this effect, uh, in practice, uh, we see that this is the traditional, uh, FL training strategy called Fed Average. So, when we increase the number of stragglers in the system, uh, uh, we observed that, uh, it takes more time, uh, to train the model as well as the accuracy of the final global model actually, uh, is reduced significantly less as compared to a scenario when there are no stragglers, uh, in the system. So, how do we account for that? So, uh, we developed and integrated, uh, a strategy called as FEDLISCAN into FEDLISCAN. Uh, so, FEDLISCAN is a semi-asynchronous clustering-based strategy, uh, specifically tailored for serverless ferret learning and can dynamically adapt, uh, to the client's behavior at very little to no communication cost. So, it contains two parts. Uh, first is an intelligent client selection algorithm based on DB scan clustering. And the second is a staleness of where aggregation to asynchronously aggregate delayed client updates. So, uh, our intelligent client selection scheme contains a lot of features that distinguish it from other approaches. First, it is based on DB scan which like, unlike other clustering, approaches does not need, uh, the number of clusters to be determined before training. And if you look at the complexity, it's N log N. So, N is the number of clients, uh, so it's suitable for large-scale, uh, ferreted learning systems. And, uh, we also consider failures and slow updates such that we group clients, uh, with similar behavior together to minimize the effect on stragglers, uh, on the rest of the clients. So, how does it work? So, our strategy partitions clients into three tiers, uh, rookies, uh, participants and stragglers. So, rookies are, uh, clients which have not yet participated in the FL training process and for which no behavioral data exists. Uh, participants are a group of clients that can participate in the clustering process. Uh, stragglers are clients that have missed one or more consecutive training rounds. Uh, these clients have the lowest priority in client selection process. So, to better explain, uh, selection strategy, assume that all clients are in a jar. Uh, this jar can be further divided into three jars based on the tiers we just mentioned. Uh, the rookies jar contains the clients which have never been called before. Uh, essentially all clients are rookies in the beginning. The second jar contains, uh, clients which participate in the clustering process and the third jar contains the unreliable clients. So, for client selections, jar, jars are ordered by a priority. The strategy for picking the clients from the three jars differs. We use random selection for rookies and stragglers, uh, while the second tier clients are selected based on clustering. So, the priority aggregation function does the following. It takes clients from the first jar, then moves on to the next, uh, if there are not sufficient number of clients available. Moreover, as the training progresses, uh, the clients can switch between the different tiers. So, after a few training rounds, the rookies jar will be empty when all clients have been called at least once. Uh, we now have few of them in the second and the third tier. During the training, we move clients between these two tiers, uh, based on a cooldown variable. Uh, this is set to demote clients which fail, uh, for a certain number of rounds to the third tier, and once it expires, they can move back, uh, to the second tier. So, how does clustering actually take place? So, uh, for clustering, uh, from the second tier of clients, we collect, uh, training times and mistrown ratios. Uh, following this, we come- compute a penalty factor called, uh, uh, mistrown ratios. Basically, this factor gives a higher penalty, uh, to, uh, clients which makes mist recent rounds. So, next, we do, uh, exponential moving average on both lists and get a single value, uh, for training EMA and mistrown EMA. So, we automatically- we provide a way to automatically tune the parameters of DB scan to give the best clustering. And after we obtain the clusters, uh, we compute, uh, total EMA, uh, for each client by adding the training time and the penalty time. Uh, we then start the clusters and pick the client closer to participate in the training process. And to accommodate for slow updates from some clients, we, uh, provide a stainless aware aggregation scheme that includes past updates, uh, but with a dampening effect. Uh, the aggregation is triggered at the end of each round. Uh, so instead of finding only the most recent round results, uh, in the parameter server, we fetch all updates newer, uh, then a certain tunable parameter, uh, called tau. Uh, following this, we aggregate the updates, according to the shown equation. So how does, uh, the training workflow in FEDLESS look like, right? So, uh, so the FL admin, uh, configures, uh, the training process, uh, the controller then requests, uh, a new invocation token from the authentication server using the credential configured by the FL admin. Uh, then, uh, it fetches, uh, basically, uh, the client behavioral results from the FEDLESS database. So this depends on the training strategy. For example, we've had average, uh, which does random selection of clients. This step will not be formed. Uh, then it invokes the, uh, intelligently chosen, uh, FL client during the round using the controller. Uh, in the next step, uh, the clients, uh, contact, uh, the authentication server to check, uh, uh, basically to validate the token. And on successful validation, uh, the client fetched the latest global model, uh, from, uh, the parameter server and then compute, uh, local model updates. So when the training is complete, the clients upload, uh, the new local model to the parameter server, uh, and they also upload some, uh, behavioral data which we need for this intelligent client selection process. So, uh, at the end of the training round, the controller adjusts the participant's behavioral attributes, uh, based on the strategy and then pushes them back to the fellow's database. Uh, afterwards, we invoke, uh, the aggregation function which combines the client's results into, uh, new global model. And at the end of the round, uh, the controller invokes a subset of clients again, uh, to essentially calculate the updated model accuracy. So this process essentially repeats, uh, until, uh, configured accuracies reach, uh, on the global model, uh, configured by the FL admin. So I would like to present some, uh, experimental results, uh, which we have performed, uh, uh, using our system. So we have experimented with a wide variety of datasets, uh, right, uh, which are also available, uh, in our repo and can be easily configured with our system. Uh, some of them are mentioned here, but we also support, uh, many others, uh, and other datasets can also be added and experimented with, right? So, but first, let's look at if this whole idea actually, uh, makes sense, right? So, uh, let's look at a fast-based system and, uh, traditional infrastructure-to-service-based system, right? So, so what we did was essentially we deployed 100 clients on the same hardware with same CPU capabilities, uh, one time using a traditional FL framework, uh, called Flower, uh, using Docker and C Groups and, uh, another time, uh, using open fast functions, uh, right? Uh, so what we found out was that, uh, our fast-based approach is actually a bit slower as compared to traditional, uh, IES-based approach. So, uh, this can, it's, this is essentially because of, uh, overhead due to the function of the service platform itself and lots due to the pure nature of functions, right? Cold starts, no persistent storage, and also due to the parameter server communication. But if you look at cost, right? So, we calculate cost, uh, using the Google Cloud Functions, uh, costing model, and we don't calculate point estimates, but we calculate, uh, broader bounds. So, for this in our calculations, we include how cost would change if the Federalist client functions took, uh, two times or three times as long as the Flower clients, uh, and if the Flower clients only took like 0.5 times as long. So, for all these experiments, we observed that the clients, uh, fast was actually cheaper. However, when we increase the total number of active clients on a total number of clients in the part of Federation, uh, fast becomes, uh, less, uh, cheap. So, this is intuitive because, uh, if you have better resource utilization in VMs, uh, the most cost efficient they are as compared to serverless. However, uh, these results are really promising, uh, since in ferrated learning, there can be, uh, millions of devices in a Federation and with only a few hundred which are active, uh, in each round. Right. So, some results with our, uh, straggler mitigation strategy, so these experiments will all perform on, uh, Google Cloud Functions, uh, for up to 200 concurrent clients, uh, in each round. So, we observed that our strategy achieves faster conversions as compared to, uh, different strategies that we, in this case, do, fed prox and fed average, uh, and on the right, we show the results for the Google speech dataset. Uh, so for the standard and the scenario with 70% stragglers, our strategy achieves faster conversions and across all datasets, which I mentioned in the slide before, we actually achieve, uh, 2% higher, uh, end accuracy. Right. So, if you look at training times, right? So, our strategy reduces over training time by 8% on average and these values in the plot represent, uh, average across all different straggler scenarios, uh, for a particular dataset. So, we observed that our strategy is definitely, uh, faster. And if you look at training costs, uh, our strategy actually leads to 20% reduction, uh, across all datasets and, uh, scenarios. So, uh, just to give a brief idea of what we are currently doing, uh, in this project. So, uh, one of the most implicit assumptions, uh, in current effort systems is that all FNL clients must have, uh, uniform ML model architecture, uh, to train a global consensus model. Right. But however, uh, this assumptions, uh, fails to address fundamental client level challenges in practical, uh, FL systems. So, uh, for instance, uh, clients in FL cannot significantly skewed, uh, non-ID data distributions, right? So, as a result in, uh, extreme non-ID scenarios, uh, the uniform global model, uh, may lead to poor generalization performance following the model aggregation process due to higher variance, uh, among the trained, uh, client models. So, in this case, uh, across the three model architectures, uh, we observed, uh, 27.1 percent decrease, uh, average decrease, uh, in the accuracy. Right. For the case with uniform data distribution and non-ID. So, uh, towards this, uh, we are looking into, uh, KD techniques, uh, to train heterogeneous personalized, uh, client models. Uh, so knowledge distillation is a popular technique, uh, used in ML, uh, that faci- facilitates the transfer of knowledge from a large, uh, and complex model known as the, uh, teacher model, uh, to a smaller and more efficient model refers to as the student model. So, uh, in this case, uh, you don't even need to exchange the model parameters or the weights, uh, but only the class logits, right, are exchanged between the clients and the central server. So, uh, we actually have, uh, integrated, uh, popular federated, uh, uh, knowledge distillation technique called FEDDF. So, it's a server-side knowledge distillation technique, uh, more details about that, uh, can be found, uh, in, you scan by scanning the QR code and converted it into a completely, uh, serverless architecture and integrated that into FEDLIS. And we have observed some promising results, uh, for example, uh, so here, alpha is essentially, uh, like a Dirichlet distribution for controlling the skew of the class labels, uh, in each client's data distribution, right? So, alpha, lower alpha value says that, uh, there is a lot of, uh, skew. So, some clients only have few class labels and also have a lot of unequal number of samples. So, when going from 100 to 0.1, we observe that, uh, the different model architectures, uh, which can be configured for each client, uh, there's not, not too much significant, uh, drop in accuracy, right? Uh, so this, we are also investigating further, uh, for other datasets and other different model architectures. So, in this case, the model architectures were simply, uh, uh, multiple different layers of CNN architecture. So, two-layer CNN, three-layer CNN, uh, stuff like this, right? Uh, great. So, more details about our work, what we're doing can also be fine found in these two papers, uh, right. And, great. So, thank you, uh, uh, for attending this session today. Uh, if you have any questions, I would be happy to answer. Thank you. Uh, that was a very fascinating talk, uh, and I had some questions, uh, as I was watching it, but you covered a lot of it, uh, throughout. Uh, one question that I still do have is, how do you, uh, account for concept drift or distribution drift, uh, across time? So, like how the average gradients can, uh, diverge? Yeah. Or like, uh, just the data distribution can diverge, uh, across the clients participating in, uh, yeah. So, uh, uh, uh, so it depends, depending upon the non-ID-ness of the distribution across clients. So, what we have observed, uh, Feliscan converges quite well, uh, for, uh, even higher non-ID data, non-ID scenarios. Uh, but, uh, in some cases where there's too much skew, uh, it doesn't converge that well. So, in that case, we're looking at KD techniques, which they are more robust. But that's a really active research problem in FL. So, uh, no one has a complete answer to that. It depends on the strategy, the aggregation scheme and a lot of other factors and what client architectures, model architectures you use. So, uh, simple averaging techniques definitely diverge, right? So, that's why I think, uh, KD and personalized thing, client models, depending upon the data distribution of a particular client is the way to go. So, personalized client models is the way to do it. Like, training a global consensus model will not always work with extreme non-ID scenarios. Yeah, so that's why we are looking at client model personalization using, uh, KD. Okay. That makes sense. Thank you. Sorry for the delay, first of all. Anyway, I, I saw about the, the results regarding the, the quality of the model itself. But what about the, the overhead in the training? I mean, when you need to share the weights all the time, I suppose that it impacts directly in the, in the training time. What about that? So, that already is accounted for that. Yeah, I'm talking about the, the training phase, and not just the results because in the, in the last slide you, you show the, the results in the, in the, in the quality. Yeah. So, uh, there was a plot with training time, which already included this. Ah, sorry. Sorry. I miss it. Yeah. Sorry. Thank you. Great then. Thank you.


A bill for an act to scrap the National Youth Service Corps NYSE, which was introduced in the House of Representatives, have elicited response. Sponsor of the bill, Awaji Nimbek Abyante, is seeking to repeal Section 315, Subsection 5A of the 1999 Constitution and the National Youth Service Corps Act. Plus, TV Africa's correspondent Jacinta Obyuku has more. Many fresh graduates earnestly desire to wear the National Youth Service Corps NYSE uniform. It is usually an honor to wear the khaki served once father learned then, and the corps members truly served with sincerity and honor. Well, that seems to be in the good old days in the 70s, 80s, 90s and even early 2000s. Now, the usefulness of the NYSE scheme has been called to question. According to the synopsis of the bill introduced by member and House of Representatives, Awaji Nimbek Abyante, who pointed out that incessant killing of innocent corps members in some parts of the country due to mandatory religious extremism, ethnic violence, incessant kidnapping of innocent corps members across the country justify the intent of the bill. Today, what do we have? Even with the challenges of ASU strike and all of it, some students spend as much as 8 to 9 years before the graduates, so far to be honest. And when they graduate, it is no longer automatic that we just walk in and answer. In some cases, people are kept on the queue for 2 to 3 years. You sum all of this up.  And most establishments will have it as a policy that you don't employ persons beyond setting age limits. There are challenges with this scheme, so whatever way it goes, it's going to be a win-win for everybody, but my stand is let us scrap it. A public analyst also gave his take on the bill. The answer is scrapping it, or you look for a way to redefine the use of the scheme. Now, if areas are challenged with respect to insecurity, then what do you do? Such areas, it does not mean insecurity will be there for forever. So you can, in the interim, alter the dynamics or the operational dynamics, I would say, of the scheme. Because a lot of youth coppers have gone to other parts of the country and never went back. They got married, they stayed there, and those are the things that have helped us to integrate as a nation. I don't think that the scheme should be scrapped. There are many things not working with it.  Here is also some mixed reactions by some Nigerians across the streets of Lagos. About the month or so, I don't see any reason why any Nigerians should not be given the chance to serve their country. Scrapping NYC still doesn't solve the problem of insecurity, right? Letting it continue still doesn't solve the problem.  So this also affords those going for the NYC to choose a better handwork to do.  They will come there to tell you that headsmen, whatever, Boko Haram, has come to eliminate their own children, not our own children anyway. NYC scheme was set up in 1973 by the Yakubu Goan administration as part of strategies to foster unity in the post-Civil War era. Jacinta Ubuku, for PLOS TV, Africa.


All right, our first question is from Jesse Jesus. What are some best ab exercises that will help with mind-to-muscle connection? I'm having trouble with my lower abs. What was the name of the YouTube video that we did? Hip flexor deactivator. Yeah, so okay, so let me address first the whole lower ab thing. Your abdominal muscles have kind of two attachments. The one attachment is at the pelvis, another attachment is at the rib cage, and so when the abs contract, it's that whole area. Yeah, there is no lower or upper abs. There is no other attachment. So I know a lot of people say, do this exercise for lower abs, or this exercise for upper abs. This really worked that way. The abs all contract, or they don't, and the two anchor points are at the bottom and the top, and that's it. But as far as connecting to the abs, this is actually quite important. In fact, more people have issues connecting to their ab muscles than don't. Just feeling them burn doesn't mean you necessarily connect to them very well. There's a lot of muscles that can fold the body forward. So when you look at ab exercises in general, especially the classic ab exercises, they involve folding your body forward. But you can also fold the body forward using your hip flexors, for example, and those tend to be more active in most people. The abs fold the body forward at the lumbar spine, not at the hips. That's what will work the abs. So one of the things you want to do to help connect to your abs is understand the function of the abs. It literally rolls you forward at the lumbar spine, at the lower part of your spine, not at the hips. So if you do a leg raise, the raising of the legs isn't working your abs, it's the rotating of the pelvis that works the abs. If you do any kind of a sit-up, it's the rolling forward that works the abs. It's not the folding forward necessarily that works the abs. So consider that first. But the most common reason why people don't feel the abs connecting is because the hip flexors are doing a lot of the work. And I did that video that Adam talked about that I called hip flexor deactivators.  And what you're doing is you're activating your glutes. You want to squeeze your glutes. Now the reason why you want to activate your glutes is because when you activate one muscle, it helps to relax the opposing muscle. And the opposing muscles from the glutes are the hip flexors. So now that the glutes are active, the hip flexors are more likely to stay out of the exercise. And then you can practice slow crunches. And this will help you feel the abs rather than do movements with your crunches. Serene also did another video with an assisted perfect setup, which I love to teach. I think that's a great way to teach somebody to really activate their abs and slow it down. A lot of times when people struggle with feeling their abs, it's like a speed thing, right? They're just using momentum and they're rocking their head and their neck and they're like sows and using so many other muscles to get them up, get the exercise up. And they feel a little bit in the abs because maybe on the weight. Stabilizing. Yeah, stabilizing or the weight down, right? As you go back down on the floor, you feel them a little bit. And so you assume that you are working them, but they can be worked so much better. And just by slowly rolling the spine up like in a perfect setup, articulating that. Is that perfect setup or McGill setup? Is it the same thing? Yeah, I don't know if it's called a McGill setup. I don't know what a McGill setup is. I've seen different people call it different titles. I know I think Serene titled it assisted perfect setup when she did the video. It's a really good video because it's already hard. I think most people are challenged even to one. Sure. So using a band to kind of like help assist you up so you can really focus on the rolling of the spine, I think is a great exercise. Yeah, it's educating too to see kind of when you start to articulate each one of those vertebrae, like where the sticking points are, where it's extra hard for you to get some in that strength to kind of curl your body into that position. But yeah, to learn that process of being able to actually roll forward and use your abs, you know, in that direction is eye-opening when you get it down. Oh yeah, physio ball crunches are good at this. But you know, here's the other thing with physio ball crunches. So make it so that you don't really work your abs very much at all. Oh, easily, yeah. So if you get on a physio ball and if physio ball crunches, by the way, one of my favorite ab exercises, if you do them properly, you put your lower back on the top of the ball, bend your legs and then put your feet on the floor and then push your butt up.  So now you're kind of arching back and then crunch over the ball while pushing your hips up. And then help anchor the hips and kind of get those hip flexors out of the movement and help you focus on the abs. Yeah, that's one of the biggest things I see people misusing it is they'll start dropping their hips down and letting their hips kind of move with and roll with the exercise. And they start just rocking back and forth.


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Hey what's up folks, in this project we're making a MIDI controller with arcade buttons. This uses the Raspberry Pi Pico to make a USB MIDI controller that features 16 LED arcade buttons. The Raspberry Pi Pico is a low-cost microcontroller with a powerful new chip, the RP2040. It features tons of GPIO and it has circuit Python support. The AW9523 is an I2C GPIO expander and an LED driver that features 16 IO pins. This lets us drive all of the LEDs so we won't run out of pins on the Raspberry Pi Pico. As a native USB MIDI device, it can work with both hardware and software that have MIDI support. The electronics are housed in a 3D printed case, and it features a handle that doubles as a kickstand. We designed the case in three parts that snap fit together so it's easy to pop it open and close it up. We think it looks a bit like a lunchbox. What makes this different from other MIDI controllers is the ability to change and save MIDI notes directly on the device. It features an OLED display and a joystick so you can quickly remap MIDI notes which is great if you're crafting your own setup. The buttons are shown as circles with numbers that represent the MIDI notes. You can use the joystick to select a button, it works just like a D-pad from a game controller. You can press the center of the joystick and select the button to edit the MIDI note. In edit mode, the button will blink letting you know that it's been activated. You can then change and update the note to go higher or lower using the joystick. While in edit mode, you can also press the buttons to compare the MIDI notes. The handle is 3D printed as a single part and it features two built-in hinges. It's a print-in-place part that doesn't need any support material. The hinges are able to freely rotate and it features mounting holes for attaching to the case. You can get the parts to build this project, links are in the description. Let us know in the comments if you'd like to see this available as a kit. The parts can be 3D printed without any support material using PLA filament. We designed the case in Fusion 360 and used 3D models of the electronics and components. It's a parametric design that features user parameters so it's easy to make adjustments. We've open sourced the design so folks can modify, remix and customize it. The code for this project was written by Liz Clark. You can check out her projects on her YouTube channel, BlitzCityDIY. This uses the MIDI library for CircuitPython and it features lots of comments so it's great for folks who are just getting started with programming. The code walkthrough by Liz is very well documented and it breaks down all the features so you can learn how to customize it. We hope this inspires more folks to get into programming electronics with CircuitPython. Be sure to check out our Learn Guide for a full step-by-step tutorial on building the project. The Learn Guide has the code, wiring diagrams and CAD files so you can follow along and make your own. Thanks so much for watching and don't forget to subscribe for more projects from Adafruit.


Hey, welcome back to the prosperity alchemist channel I'm David Wong and we're gonna go over the first three levels of the prosperity pyramid very quickly so you can overview of What is covered in this series? So let's go to look at number one Level number one. So level one is the life force flow So how to enhance your life force in the middle? We have a diagram of the flower of life the flower of life is based on sacred geometry Everything on the planet everything is you know on the universe is based on this flower of life geometry Everything in nature is based on this pattern now This pattern is actually a special flower of life You won't find this anywhere because people say well there's something wrong your flower of life because it's not symmetrical Well, the reason is not symmetrical because we have a special algorithm to calculate a Flower of life that has a differential and look at flower petals. It's not symmetrical if you look at corals It's not completely symmetrical. So nature itself is not completely symmetrical Why is that? It's because there is a flow of energy wherever there is a differential Let's say you have something high and something low and in order to have I mean that is why it creates Any kind of movement any kind of Energy is you need to have a differential you need to have a negative and a positive in order to create any kind of Flow, okay, so that's what this diagram is now in order to Build your life force and instead of talking about how to get more energy how to get more How to study Qigong how to practice Qigong how to raise your Qi which is basically what this is about also why not first Stop all the draining and negative programming. So you see on the left here It says negative programming and drain so imagine you're filling a bathtub of water You don't want to fill out a bathtub that has a whole bunch of holes in it You just waste money waste time and waste waste resources waste your energy and waste water Okay, so first let's plug up all those drains of life force that life flow that could be happening to you So frequencies music EMFs all these are baseline frequencies audible frequencies visible frequencies and non audible non visible Refrequencies, okay, but you can certainly feel them Low life force food stress and healthy lifestyle negative people negative environment So those are drains and this goes on there's probably a lot more things on that list that cause drain And you can probably think of what those are so once you plug those holes, then you can start to amplify by By exposing yourself to positive frequencies positive music positive even mess. Yes, there's positive even mess that you can have There's a technology I can show you that I'm using that Really enhances your positive a ff energy. Okay, so and then supplements high life force food Meditation martial arts positive people positive environment. So that's level one of how you can start generating life force Generating she in your life By doing those things So let's look at number two Alright, so level number two is your life force engine How to harmonize your mind body heart and spirit So yes, you're this is like an engine and in order for any kind of engine to run properly It has to be tuned Okay, for example like in the car a combustion engine you have pistons now if the pistons are not lined up and not Time properly then basically pistons are go look go against each other and the car is not going to go anywhere And you're just going to wreck your engine the same thing goes with Your mind and body. Okay, if you have your pistons and your mind and body spirit and heart firing That's not synced up then you're just going to wreck it and it's just you're just gonna waste energy waste your Waste your life force that you generated in level one. Okay, so how do you balance your internal external your mind body? Heart and spirit. Okay.  Is it okay? So the reason we want to align it up straight up and down is so that there's no resistance and so that it goes just It's a very becomes a more efficient antenna and becomes a more powerful and more effective way to communicate to Higher self can they take to God may you create to the universe and so on okay, so How to circulate she how to have relaxed awareness and breath control So those are some things that we'll cover in level two of what you can do to harmonize your mind body heart and spirit Okay, and then how to activate your Chi electric magnetic. So Chi is I'm going to show you is based on actually Electrical magnetism in your body and also based on Actually a little bit more than that, but or the most part is electrical magnetism so let me show you how to increase the power of the engine and increase your manifestation ability because this is what it is right because when you're like I said you're an antenna and the stronger the projection of the antenna the stronger the let's say the reach or the Cell phone range for it so to speak of your antenna then the more power you have Towards your manifestation abilities. Now. Let me show you this new Software and this new technology that I've been using that helps you to kind of visualize what I'm talking about All right, so here's some new technology that I've been using is called the GDV camera and What it does is it takes pictures of your aura with your hands? We all have an aura field whether you believe in that we all have energy field and for a long time people said Oh, that's bullshit. It doesn't exist. But now we can scientifically prove and measure this aura field and People say well as pseudoscience not real little science. It's pseudoscience, right? You can't believe in that crap, right? So well first of all, what is science science is needs three criteria number one has to be Deplicatable number two has to be repeatable number three has to be quantifiable has to be measurable. Okay. Well, this is it okay, let me show you so For example, I've been doing some training and as you can see here Okay, so what this does is it measures your energy fields your chakras your balance and then your Energy scan it shows you your health status and energy users So if you want to know whether you have a strong life force if you have your chi is low or high and Whether your engine is running properly Life force engine then this is exactly what you need to take a snapshot of that to see what's going on Okay, so here you can see an energy field for example You can see it's all around the body. There's a left view. There's a right view Okay, and then when there's a spark that means there is some extra activity there Ideally you want something that's very even without too many sparks and very full Okay, and Let's see chakras. It also shows you the chakra. So for example The size of the chakra shows you how much energy is in chakra and then whether it's to the left or right Shows you whether it's aligned to yin or line to yang So you can see right now it is alignments 93% so it can have improvement You see the throat chakra is extra strong and then maybe this one's a little bit weaker. Okay, so That's how you can see the alignment of your chakras in and yang you see How balanced your yang and yin is and that's based on the energy that's coming out of your organs your heart your lungs the liver meridian screen Meridian kidneys pericardium small intestine Argentestine gallbladder stomach triple warmer bladder energy So that's all based on traditional Chinese medicine with the meridians in your body Okay, and also shows you your Energy level emotional pressure whether you are calm optimal anxious stress heightened or or just like too much pressure and energy it shows you low Lower optimal height and high. So is your symmetry how balance you are from left to right. So do you how balance your organs are? And it shows you your health status So each one of these lines on the circle is one of your meridians Or one of your organs and then the how far out it is depends how determines how much energy it is So if it's too close to the center, that means that it is not Healthy and it's very weak on energy. So ideally you can click on here You can see it optimally you want to be and where the green line is Yes, and you see whether you're balanced on the left or right and Energy to reserve you can see How much energy reserve so 85% so optimal energy reserve once again is right in there in the optimal level and Maybe I see it was too active with the throat. Maybe I'm talking too much. So you got too much energy there and Probably not enough energy Looks pretty good Maybe too much energy in some places. Okay, so the cool thing is that you can do one of these tests And then later on in the day you can do after you do some training or after do some meditation You can do another one and you can compare it. So let's just select these two click on compare And then we go here and then we compare them. You can see there's any change So this was let's see we did this On Yeah, so about an hour later. I was I did some meditation. I'd use some of the technology with my life force life force harmonizer Technology and you can see that it balanced out my energy level now the energy is measured in jewels You can see here and I left is that 66 jewels 10 to the negative 2 so that's point 66 jewels of energy Okay, and my energy level went up by five Decaduels you can call it. I think it's decaduels. Yeah, so So it went up the energy and the phone as you can see it went up And then some of the spikes are gone and kind of evened it out a bit But in generally had more energy the balance usually goes down whenever you have a change in energy level Because things are moving around eventually it will balance out. So this is only just an hour later Okay, and you can see the comparison of the energy fields and The chakras. Let's see we get more line. That's the line So yeah, I went less the line. There's more aligned before and then went less the line from it went from 93 to 89 and that's pretty common whenever you increase your energy levels.  So my emotional pressure went down So it became more calm and more relaxed and right in the perfect optimal position right there Okay, so it was an almost anxious and anxiety and From the meditation and from my technology. I was able to bring it down to basically perfect Energy went up. So I went to even a little bit heightened energy here And my like I said my symmetry went down when that's normal whenever there is a increased in energy my organs became a little bit more balanced and And you can see it kind of balanced out my energy levels. Let's take a look at this So on the left side, that's what it's before and this is after So you can see how it became more round. So the rounder it is the better This means that your organs are more balanced and the right side looks very good here before it was Less than optimal here, but then after the session It was very optimal here on this side almost perfect circle Energy my energy level went up before it was 85% and then after that went up to a hundred percent energy level Which is great. So you're low in energy. You want to have an energy charge. This is one a good way of doing it it's just by Using this new technology I have right now and the cool thing is that now and there's no guesswork It's not like oh, I feel better and then people can't really quantify it now You can see on the chart. You see exactly how much By how many digits by how many percent it improved and Here's the balance. So, you know, oh highlight the ones that are a little bit more less balanced and the rest of them here You can see our balance okay So how this works is it takes pictures of your fingers and in Chinese medicine even in ayurvedic medicine Your fingers and if you have divide your fingers into quadrants like this dissections like a pizza each part of your finger determines a certain part of your body Okay, and depending on how much energy. Yeah, so that's what this this this device Does this camera does is actually can capture the energy levels that are coming out of your fingers Okay, so I'll go more into that into in the net not a lesson because I got a lot of technology behind it But that's why it does it can take a picture of the energy electromagnetic energy coming from your fingers Okay, and then here's the graph of the left energy level went up Front energy went up right energy went up. Blimey it went down a bit. Okay Yeah, so I just want to show you that so you can see what I mean about being able to increase your electromagnetic energy and be able to Enhance your life force engine and now we have technology that can show you exactly how to enhance it and what part of your Body or meridian or chakra or orient needs extra energy or needs Extra attention so that you can get that Life force engine to work properly free. Okay, so stay tuned and we're gonna show you the level three of this Prosperity pyramid. So here's level three the metatrons success formula How did transmute your thoughts into reality? So so far we've been talking about the engine and an engine is what you need in order to project in order to get The energy and convert it into kinetic energy convert it into reality So this is what we're doing with level three we're converting all your thoughts all your dreams all your aspirations into reality Okay, this and here's this steps that you need to do in order to Do it there's six steps as you see in this diagram is got our II PAE and that stands for responsibility identify identify plan evaluate and repeat Okay, so what's responsibility responsibility means that you need to take responsibility for yourself No one's going to help you be successful No one's gonna help you to transmit your thoughts into reality except you you need to take responsibility and To claim that for yourself and not rely on Somebody else or count on something else. Okay, number two you need identify what you want You're gonna really think about what you really want if you can't think of what you really want to think about what you don't want Which is number three So identify why you want identify what you don't want those are the two eyes and then number four make a plan How are you going to? How are you going to? Achieve the next step make a plan Okay, and then take action too. So once you make a plan then you gotta take action You got to put it into put the work into it You need to do something take action and then the next one is to evaluate and the next one is to Repeat okay, so it should be r i i p a e and Another are well doesn't need to be so you just repeat again. Go back to one. Okay, so That's what you need to do. So what is what metatron if you study metatron's cube. It's based on this You can see here. There's a cube here if you look at that There's a cube. That's the side of the cube. There's another side. That's the top of the cube now We connect all the lines together you get something called the Five different shapes in here first of all there's a first one is the triangle. That's obvious and then you see in 3d you can see the illusion of the what we call the Equilateral triangle. I forgot what it's called Basically as a pyramid that is based on a triangle and the next one you're gonna you can see is the ice Ice ice saucer hedron and the dodeca hedron. Well, basically what all that means is that you can see the 3d shapes in there And those shapes are the basis of the molecular structure all atoms in existence Okay, everything in that we know of exists based on those Three-dimensional shapes that show up in just this diagram here Okay, so that's why I called it metatron success formula because there's six sides to this And based on these six things you can basically create anything that you want in the universe Okay, you can based on these things you can transmute your thoughts into reality Okay, so in future lessons We're gonna go on to each one of these points on the metatron success formula each of the six points And then go into detail how you can do each one of those things and how they make that work for you To help you to achieve your success. Alright, so that's the prosperity pyramid the first three levels There's an overview of what you can do in order to achieve success and achieve prosperity I have broken it down into first three levels being your life force how to generate life force and then level to how to balance your your mind body soul and spirit as an engine and How to how to gain success skills and the metatron success formula the six steps for you to achieve Success and put your vehicle into motion and to gain momentum so that you can achieve prosperity So if you put all these levels together and have them working for you, then you can achieve prosperity automatically and effortlessly and And the basis of all this is life force so make sure you come to my webinar to check out this new technology and new techniques that you can use to Automatically gather life force and to build that life force that you need in order to be the foundation to achieve anything that you want and to Manifest anything that you want in your life So if you have questions, make sure you put them in the comment section and tell your friends about this video Share it with everybody that you know and subscribe to this channel Okay So and make sure you show up to my webinar is coming up real soon on how you can use some technologies that have Develop that help you to generate life force automatically without even lifting a finger So we'll see you soon in the next lesson and until then use the force and prosper


Hepatocellular carcinoma, HCC, is one of the most common types of liver cancer, with over 90% of cases being caused by chronic liver disease. Chemotherapy and immunotherapy are the primary treatments for HCC, but they often come with serious side effects due to their systemic nature. Localized chemotherapy has been shown to be more effective at reducing these side effects, as it delivers the drugs directly to the tumor site. One way to do this is through the use of biodegradable hydrogels, which can release the drugs slowly over time. These hydrogels have been found to be particularly effective in treating HCC, as they allow for greater control over the amount of drug released and the rate at which it is released. Additionally, they can be designed to respond to various environmental factors, such as temperature or pH changes, making them even more effective. This article was authored by Bihari Farisadi Far, Alia Tarapouris Fahani, Alma's Nazarian, and others. We are article.tv, links in the description below.