Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing
This work addresses security and efficiency challenges for decentralized learning in mobile edge networks, such as connected autonomous vehicles, though it appears incremental by combining existing technologies.
The paper tackles the high communication costs and security vulnerabilities in synchronous federated learning and blockchain for mobile edge computing by proposing an asynchronous blockchained federated learning framework (BFL-MEC) with post-quantum security features, achieving stable global learning and defense against attacks as validated through simulations.
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.