量化个性化程度对联邦学习精度与通信成本的权衡，指导实际选择。
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees
- 提出统一框架，同时学习全局与本地模型，量化个性化程度影响。
- 证明在非凸设置下理论最优性，实验证明在合成与真实数据上有效。
- 适合关注联邦学习效率与个性化平衡的研究者和工程师。
个性化联邦学习（PFL）为异构数据分布下的分布式客户端提供了灵活的信息聚合框架。本文研究一种同时学习全局与本地模型的PFL设置。纯本地训练无通信开销，但客户端协作可利用共享知识提升统计精度，从而产生精度-通信权衡。然而，个性化程度如何定量影响样本与算法效率及其内在权衡，尚缺乏系统理论分析。本文填补这一空白，提供个性化程度对权衡的定量刻画，并给出个性化程度选择的理论依据。作为附加贡献，本文建立了广泛研究的PFL形式在统计精度上的极小极大最优性。理论结果在合成与真实数据集上得到验证，且在非凸设置中具有泛化能力。
原文摘要 · Abstract (English)
Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, collaborative learning among the clients can leverage shared knowledge to improve statistical accuracy, presenting an accuracy-communication trade-off in personalized federated learning. However, the theoretical analysis of how personalization quantitatively influences sample and algorithmic efficiency and their inherent trade-off is largely unexplored. This paper makes a contribution towards filling this gap, by providing a quantitative characterization of the personalization degree on the tradeoff. The results further offers theoretical insights for choosing the personalization degree. As a side contribution, we establish the minimax optimality in terms of statistical accuracy for a widely studied PFL formulation. The theoretical result is validated on both synthetic and real-world datasets and its generalizability is verified in a non-convex setting.
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