Managing Federated Learning on Decentralized Infrastructures as a Reputation-based Collaborative Workflow
Yuandou Wang, Zhiming Zhao
TL;DR
This paper addresses the challenge of managing Federated Learning on decentralized infrastructures with heterogeneous and potentially untrustworthy participants. It proposes a reputation‑based collaborative workflow that combines open workflow standards (CWL), blockchain‑enabled smart contracts, and contract theory to automate FL pipelines, incentivize high‑quality contributions, and maintain reliable training despite attacks. A dynamic reputation system, committee selection, and a multidimensional contract framework are developed to align incentives, penalize misbehavior, and optimize rewards, with theoretical analysis and extensive simulations validating fairness and robustness. The work demonstrates that encoding FL operations as FAIR CWL workflows and enforcing contracts on a blockchain can enable scalable, secure, and fair decentralized FL deployments, with future work focusing on real‑world on‑chain/off‑chain demonstrations and smart‑contract implementations.
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.