多源数据的分布鲁棒联邦学习
Distributionally Robust Federated Learning with Multi-Source Data
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中文总结 AI 辅助
针对联邦学习中客户端混合比例未知及客户端内分布模糊问题，提出构建全局模糊集并开发联邦算法，提供样本外性能保证并验证有效性。
中文摘要 AI 辅助
联邦学习利用私有客户端数据训练共享模型。在实践中，数据生成分布可能不同，且客户端间的真实混合比例往往未知，使得潜在的组分布难以明确指定。现有方法通过针对最坏情况混合进行优化来处理跨客户端混合不确定性，但假设客户端分布估计准确。然而，基于有限样本的估计可能不可靠。为了同时处理跨客户端混合不确定性和客户端内分布模糊性，我们构建了一个全局模糊集，作为局部模糊集的可容许混合的并集。该构造允许客户端特定的模糊半径，并支持客户端可分离的重新表述。利用这一结构，我们建立了高概率的样本外性能保证。我们进一步为基于惩罚的重新表述开发了一种联邦算法，并在较温和的正则条件下证明了其收敛性。模拟实验验证了该算法的有效性。
英文摘要
Federated learning trains a shared model from private client data. In practice, data-generating distributions may differ, and the true mixture across clients is often unknown, making the underlying group distribution difficult to specify. Existing approaches address cross-client mixture uncertainty by optimizing against the worst-case mixture, yet assume accurate client-wise distribution estimates. However, these estimates can be unreliable when based on finite samples. To handle both cross-client mixture uncertainty and within-client distributional ambiguity, we construct a global ambiguity set as the union of admissible mixtures of local ambiguity sets. The construction allows client-specific ambiguity radii and admits a client-wise separable reformulation. Leveraging this structure, we establish a high-probability out-of-sample performance guarantee. We further develop a federated algorithm for a penalty-based reformulation and prove its convergence under milder regularity conditions. Simulations validate the algorithm's effectiveness.
发表机构
- Peking University（北京大学）
- University of Groningen（格罗宁根大学）
机构由 AI 辅助整理，请以论文原文为准。
