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

TL;DR

This paper proposes a reputation-based, blockchain-enabled framework for managing federated learning in decentralized environments, addressing challenges of workflow automation, participant incentivization, and system reliability.

Contribution

It introduces a novel workflow management approach combined with blockchain and contract theory to enhance reliability and fairness in federated learning systems.

Findings

Incentive mechanisms achieve fairness in reward distribution.

The proposed approach enhances system reliability against malicious attacks.

Theoretical analysis supports the optimality of contract-based smart contracts.

Abstract

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 focus on the workflow-based methods to automate diverse FL pipelines and propose a novel approach to facilitate reliable FL system operations with robust mechanism design and blockchain technology by considering a contribution model, fair committee selection, 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 simulation experiments to validate the proposed approach. The results show that our incentive mechanisms are feasible and can achieve fairness in reward allocation in unreliable environment settings.