  Mobile Edge Computing (MEC) has been a promising paradigm for communicating
and edge processing of data on the move. We aim to employ Federated Learning
(FL) and prominent features of blockchain into MEC architecture such as
connected autonomous vehicles to enable complete decentralization,
immutability, and rewarding mechanisms simultaneously. FL is advantageous for
mobile devices with constrained connectivity since it requires model updates to
be delivered to a central point instead of substantial amounts of data
communication. For instance, FL in autonomous, connected vehicles can increase
data diversity and allow model customization, and predictions are possible even
when the vehicles are not connected (by exploiting their local models) for
short times. However, existing synchronous FL and Blockchain incur extremely
high communication costs due to mobility-induced impairments and do not apply
directly to MEC networks. We propose a fully asynchronous Blockchained
Federated Learning (BFL) framework referred to as BFL-MEC, in which the mobile
clients and their models evolve independently yet guarantee stability in the
global learning process. More importantly, we employ post-quantum secure
features over BFL-MEC to verify the client's identity and defend against
malicious attacks. All of our design assumptions and results are evaluated with
extensive simulations.
