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

2025-02-28

AI Summary

To address three key challenges in decentralized federated learning—workflow automation difficulty, insufficient participant incentives, and frequent malicious attacks—this paper proposes the first collaborative management framework integrating a dynamic reputation mechanism with contract theory. Methodologically, we design an on-chain smart contract that jointly performs contribution evaluation and fair committee election to achieve provably optimal reward allocation; we further construct a dynamic reputation update model to identify high-quality nodes and mitigate Sybil attacks. Theoretically, we prove that the mechanism satisfies incentive compatibility and robustness. Experimental results demonstrate that, under a 30% malicious node setting, the framework achieves 92.4% reward fairness, accelerates model convergence by 27%, and significantly enhances resilience against adversarial attacks.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Automating diverse FL workflows in decentralized environments.
• Incentivizing high-quality data and computing resources participation.
• Ensuring reliable FL operations against malicious attacks.

Innovation

• Automates FL workflows using blockchain technology
• Implements dynamic reputation and reward systems
• Designs optimal smart contracts for FL management