arXiv：cs.LG· Drashthi Doshi, Avishek Ghosh, Swaprava Nath·· 10 小时前
激励数据贡献与学习模型并重：具有策略性数据所有者的联邦学习
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners
AI 导读
研究者提出 UPBReD 与 2P-UPBReD 两种机制，在联邦学习中同时激励客户端贡献数据并训练模型，前者以几何速率收敛到纳什均衡，后者通过预算平衡转移使完全贡献成为占优策略。
正文
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.
| Comments: | 52 pages, under review |
| Subjects: | Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2505.12010 [cs.GT] |
| (or arXiv:2505.12010v5 [cs.GT] for this version) | |
| https://doi.org/10.48550/arXiv.2505.12010 arXiv-issued DOI via DataCite |
Submission history
From: Swaprava Nath [view email]
[v1]
Sat, 17 May 2025 14:04:20 UTC (141 KB)
[v2]
Thu, 19 Jun 2025 14:06:03 UTC (140 KB)
[v3]
Mon, 13 Oct 2025 10:24:17 UTC (151 KB)
[v4]
Mon, 16 Mar 2026 01:56:51 UTC (197 KB)
[v5]
Wed, 14 Oct 2026 09:30:50 UTC (247 KB)
来源：arXiv：cs.LG · arxiv.org