Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View

AI 中文解读

联邦学习新突破：让海量用户既能保护隐私，又能享受个性化模型。这项研究巧妙地用“平均场博弈”理论解决了联邦学习中的隐私难题——简单说，传统方法要么给数据加噪声导致模型变差，要么让用户各自选隐私策略但人数一多就乱套。新框架允许每个用户像“投票”一样独立选择自己的隐私保护力度，而系统只看所有用户的平均行为，就能高效找到平衡点。比如你在手机输入法上训练个性化词库，现在可以自由选择想让隐私保护多严格，同时系统仍能准确学习你常用的网络用语。更妙的是，它还能为不同用户提供不同强度的隐私保障，不像以前只能一刀切。这项技术未来可能让银行、医院等机构在联合建模时，真正实现“数据不出门，模型共成长”，用户再也不必担心自己的交易记录或病历被泄露。

arXiv:2607.23029v1 Announce Type: new Abstract: 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.