Designing Federated Learning Marketplaces: Incentives, Network Dynamics, and Decentralized Learning
Arisdakessian, Sarhad Wehbi, Osama Wahab, Omar Abdel Mourad, Azzam Otrok, Hadi Guizani, Mohsen
Arisdakessian, Sarhad
Wehbi, Osama
Wahab, Omar Abdel
Mourad, Azzam
Otrok, Hadi
Guizani, Mohsen
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Department
Machine Learning
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Journal article
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English
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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.
Citation
S. Arisdakessian, O. Wehbi, O.A. Wahab, A. Mourad, H. Otrok, M. Guizani, "Designing Federated Learning Marketplaces: Incentives, Network Dynamics, and Decentralized Learning," IEEE Transactions on Network Science and Engineering, vol. PP, no. 99, pp. 1-18, 2026, https://doi.org/10.1109/tnse.2026.3682956.
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IEEE Transactions on Network Science and Engineering
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Keywords
46 Information and Computing Sciences, 4604 Cybersecurity and Privacy, 4606 Distributed Computing and Systems Software
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IEEE