A Quality-of-Service Compliance System using Federated Learning and
  Optimistic Rollups
Joao Paulo de Brito Goncalves Guilherme Emerick Sathler Rodolfo da Silva Villaca
Edge computing brings a new paradigm in which the sharing of computing,
storage, and bandwidth resources as close as possible to the mobile devices or
sensors generating a large amount of data. A parallel trend is the rise of
phones and tablets as primary computing devices for many people. The powerful
sensors present on these devices combined with the fact that they are mobile,
mean they have access to data of an unprecedentedly diverse and private nature.
Models learned on such data hold the promise of greatly improving usability by
powering more intelligent applications, but the sensitive nature of the data
means there are risks and responsibilities to storing it in a centralized
location. To address the data privacy required for some data in these devices
we propose the use of Federated Learning (FL) so that specific data about
services performed by clients do not leave the source machines. Instead of
sharing data, users collaboratively train a model by only sending weight
updates to a server. However, the naive use of FL in those scenarios exposes it
to a risk of corruption, whether intentional or not, during the training phase.
To improve the security of the FL structure, we propose a decentralized
Blockchain-based FL in an edge computing scenario. We also apply blockchain to
create a reward mechanism in FL to enable incentive strategy for trainers.