Towards Blockchain-Based Reputation-Aware Federated Learning

Abstract

Federated learning (FL) is the collaborative machine learning (ML) technique whereby the devices collectively train and update a shared ML model while preserving their personal datasets. FL systems solve the problems of communication-efficiency, bandwidth-optimization, and privacy-preservation. Despite the potential benefits of FL, one centralized shared ML model across all the devices produce coarse-grained predictions which, in essence, are not required in many application areas involving personalized prediction services. In this paper, we present a novel concept of fine-grained FL to decentralize the shared ML models on the edge servers. We then present a formal extended definition of fine-grained FL process in mobile edge computing systems. In addition, we define the core requirements of fine-grained FL systems including personalization, decentralization, fine-grained FL, incentive mechanisms, trust, activity monitoring, heterogeneity and context-awareness, model synchronization, and communication and bandwidth-efficiency. Moreover, we present the concept of blockchain-based reputation-aware fine-grained FL in order to ensure trustworthy collaborative training in mobile edge computing systems. Finally, we perform the qualitative comparison of proposed approach with state-of-the-art related work and found some promising initial results.