AI Insight
This paper addresses the free-rider problem in federated learning, where clients may contribute minimal data while benefiting from models trained on others' data. The researchers propose three mechanisms (UPBReD, 2P-UPBReD, and T-2P-UPBReD) that combine model training with incentive structures using game theory and payment schemes to encourage full data contribution. The most advanced mechanism, T-2P-UPBReD, achieves truthful reporting of contributions and gradients while maintaining budget balance and reaching optimal model performance, as demonstrated through experiments on CIFAR-10, FEMNIST, and Twitter datasets.
Why it matters
This work addresses a critical challenge in federated learning deployments where participants have economic incentives to withhold data. The proposed mechanisms could enable more effective collaborative machine learning in real-world scenarios involving strategic participants, such as healthcare consortiums or commercial data partnerships, by ensuring fair contribution and preventing exploitation of collective efforts.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: In federated learning (FL), a center trains a shared model from updates that clients compute on their private data. Collecting and training on data is costly, so a strategic client may contribute little data and free-ride on the model trained by the others. We study mechanisms that incentivize data contribution and learn the model at the same time. Our first mechanism, UPBReD, updates the clients ‘ contributions and the model jointly, and converges at a geometric rate to a Nash equilibrium of the contribution game together with the welfare-maximizing model for those contributions. This equilibrium can involve under-contribution, so our second mechanism, 2P-UPBReD, adds budget-balanced transfers that make full contribution a dominant strategy, after which training converges to the optimal model; we also give conditions under which every client prefers to participate. Both mechanisms trust what clients report about the data they use and the gradients they compute. We show that no payment rule can reward unverifiable contributions unless something the center observes depends on the data actually used. Building on this, T-2P-UPBReD checks each client’s reported contribution against the other clients ‘ realized valuations of the trained model, and prices gradient reports with a budget-balanced charge. It elicits truthful contribution reports, makes gradient reports approximately truthful, still reaches full contribution and the optimal model, and never runs a deficit. Experiments on CIFAR-10, FEMNIST, and Twitter show that 2P-UPBReD matches the welfare of FedAvg trained on all data, while strategic clients without incentives lower the welfare of the learned model.
Source: Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners