Designing Federated Learning Marketplaces: Incentives, Network Dynamics, and Decentralized Learning

Abstract

Federated Learning enables collaborative model training across distributed clients without requiring direct access to their private data. However, effective deployment faces critical challenges, including heterogeneous data quality, unbalanced participation, and the lack of incentives. In this paper, we propose a federated learning network structured as a decentralized marketplace, where clients are financially rewarded based on the quality and utility of their contributions. Our framework enhances client selection through utility-driven mechanisms and offers strong incentives that promote sustained, high-quality participation. It also ensures security and transparency for the Task Owner while maintaining data privacy. The architecture can support a wide range of collaborative scenarios; spanning from healthcare and finance to consumer applications; where data privacy, fairness, and scalability are paramount. We demonstrate the practicality and effectiveness of our approach through experiments, showcasing improved global model accuracy, and equitable participation.