AI 中文总结
研究联邦学习中隐私问题，提出将其表述为平均场隐私博弈的方法，每个客户端通过平均场统计量选隐私预算，该框架能产生可处理均衡，适应异构偏好，继承隐私保证，实验表明其实现隐私 - 效用权衡并提供个性化保证。
AI 中文摘要
联邦学习允许跨分布式客户端进行协作模型训练而无需集中数据，但隐私仍是一个持续关注的问题，因为共享的模型更新可能会泄露本地数据集的信息。现有隐私保护方法要么在客户端更新中注入校准噪声，限制其组合保证，要么将客户端隐私选择表述为多智能体博弈，随着客户端数量增加，其纳什均衡变得难以处理。我们通过将隐私保护联邦学习表述为平均场隐私博弈来弥合这两条工作线：每个客户端在仅通过单个平均场统计量与总体交互时战略性地选择自己的隐私预算。平均场极限为任意数量的客户端产生一个可处理的均衡，适应异构客户端偏好，并通过对数 Sobolev 收缩继承指数衰减的隐私保证。该框架在同质特殊情况下恢复熵隐私基线，在有限总体情况下恢复多智能体隐私博弈。在二次回归、逻辑回归和 MNIST 上的实验表明，所提出的框架实现了熵基线的隐私 - 效用权衡，同时提供了同质基线无法表达的个性化隐私保证。
英文摘要
Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared model updates can leak information about local datasets. Existing privacy-preserving methods either inject calibrated noise into client updates, limiting their composition guarantees, or formulate client privacy choices as a multi-agent game whose Nash equilibrium becomes intractable as the number of clients grows. We bridge these two lines of work by formulating privacy-preserving federated learning as a mean-field privacy game: each client strategically chooses its own privacy budget while interacting with the population only through a single mean-field statistic. The mean-field limit yields a tractable equilibrium for arbitrarily many clients, accommodates heterogeneous client preferences, and inherits an exponentially decaying privacy guarantee through a log-Sobolev contraction. The framework recovers the entropic privacy baseline as the homogeneous special case and the multi-agent privacy game as the finite-population case. Experiments on quadratic regression, logistic regression, and MNIST demonstrate that the proposed framework attains the privacy-utility trade-off of the entropic baseline while delivering a personalized privacy guarantee that the homogeneous baseline cannot express.
