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arXiv Machine Learning By Drashthi Doshi, Avishek Ghosh, Swaprava Nath

Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

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The paper proposes mechanisms for federated learning that both incentivize strategic data owners to contribute fully and learn a high‑quality model. The first mechanism, UPBReD, jointly updates client contributions and the model, converging to a Nash equilibrium that may involve under‑contribution. The second mechanism, 2P‑UPBReD, introduces budget‑balanced transfers to make full contribution a dominant strategy, ensuring convergence to the optimal model while guaranteeing that all clients prefer to participate. Subsequent extensions (T‑2P‑UPBReD) further enforce truthful reporting of contributions and gradients without running a deficit, and experiments on CIFAR‑10, FEMNIST, and Twitter demonstrate that 2P‑UPBReD achieves welfare comparable to FedAvg trained on all data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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