Unsupervised anomalies detection in IIoT edge devices networks using
  federated learning
Niyomukiza Thamar Hossam Samy Elsaid Sharara
In a connection of many IoT devices that each collect data, normally training
a machine learning model would involve transmitting the data to a central
server which requires strict privacy rules. However, some owners are reluctant
of availing their data out of the company due to data security concerns.
Federated learning(FL) as a distributed machine learning approach performs
training of a machine learning model on the device that gathered the data
itself. In this scenario, data is not share over the network for training
purpose. Fedavg as one of FL algorithms permits a model to be copied to
participating devices during a training session. The devices could be chosen at
random, and a device can be aborted. The resulting models are sent to the
coordinating server and then average models from the devices that finished
training. The process is repeated until a desired model accuracy is achieved.
By doing this, FL approach solves the privacy problem for IoT/ IIoT devices
that held sensitive data for the owners. In this paper, we leverage the
benefits of FL and implemented Fedavg algorithm on a recent dataset that
represent the modern IoT/ IIoT device networks. The results were almost the
same as the centralized machine learning approach. We also evaluated some
shortcomings of Fedavg such as unfairness that happens during the training when
struggling devices do not participate for every stage of training. This
inefficient training of local or global model could lead in a high number of
false alarms in intrusion detection systems for IoT/IIoT gadgets developed
using Fedavg. Hence, after evaluating the FedAv deep auto encoder with
centralized deep auto encoder ML, we further proposed and designed a Fair
Fedavg algorithm that will be evaluated in the future work.