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.

翻译：移动边缘计算（MEC）已成为移动数据通信与边缘处理的一种有前景的范式。我们旨在将联邦学习（FL）和区块链的显著特性（如完全去中心化、不可篡改性和激励机制）集成到MEC架构（例如互联自动驾驶车辆）中。FL对于连接受限的移动设备具有优势，因为它只需将模型更新传输至中心点，而非大量数据通信。例如，在自主互联车辆中，FL可提升数据多样性并支持模型定制，即便车辆在短时间内断开连接（通过利用其本地模型）也能进行预测。然而，现有同步FL与区块链因移动性导致的性能损耗而面临极高通信成本，无法直接适用于MEC网络。我们提出一种完全异步的区块链联邦学习（BFL）框架（称为BFL-MEC），其中移动客户端及其模型独立演化，同时保证全局学习过程的稳定性。更重要的是，我们在BFL-MEC中采用后量子安全特性来验证客户端身份并防御恶意攻击。所有设计假设与结果均通过大量仿真实验进行了评估。