Managing Federated Learning on Decentralized Infrastructures as a Reputation-based Collaborative Workflow

Federated Learning (FL) has recently emerged as a collaborative learning paradigm that can train a global model among distributed participants without raw data exchange to satisfy varying requirements. However, there remain several challenges in managing FL in a decentralized environment, where potential candidates exhibit varying motivation levels and reliability in the FL process management: 1) reconfiguring and automating diverse FL workflows are challenging, 2) difficulty in incentivizing potential candidates with high-quality data and high-performance computing to join the FL, and 3) difficulty in ensuring reliable system operations, which may be vulnerable to various malicious attacks from FL participants. To address these challenges, we propose a novel approach to facilitate reliable FL system operations with robust mechanism design and blockchain technology by considering a stake-base deterrent rule, fair committee selection, malicious detection and defense, dynamic reputation updates, reward and penalty methods, and contract theory. Moreover, we study the optimality of contracts to guide the design and implementation of smart contracts that can be deployed in blockchain networks. We perform theoretical analysis and conduct extensive experiments to validate the proposed approach under different poisoning attacks and non-IID settings. The results show that our incentive mechanisms are feasible and can achieve fairness in reward allocation in unreliable environment settings.