少量-多数个性化联邦学习
Few-for-Many Personalized Federated Learning
AI总结：
FedFew通过少量共享服务器模型实现高效个性化联邦学习，自动优化模型多样性，优于现有方法。
摘要：
个性化联邦学习（PFL）旨在在数据分布高度异质的情况下为客户端训练定制模型，同时保护数据隐私。现有方法往往依赖聚类或模型插值等启发式方法，缺乏平衡异质客户端目标的原理性机制。为M个具有不同数据分布的客户端服务本质上是一个多目标优化问题，实现最优个性化理想上需要M个不同的模型在帕累托前沿上。然而，在有数百或数千个客户端的联邦设置中，维护M个单独模型存在显著的可扩展性挑战。为了解决这一挑战，我们将PFL重新表述为一个少量-多数优化问题，仅维护K个共享服务器模型（K<<M）以共同服务所有M个客户端。我们证明了该框架实现了接近最优的个性化：当K增加时，近似误差减小，每个客户端的模型在数据增长时收敛到每个客户端的最优解。基于这一重新表述，我们提出FedFew，一种实用的算法，通过高效的梯度更新联合优化K个服务器模型。与基于聚类的方法需要手动客户端分区或基于插值的方法需要仔细调整超参数不同，FedFew通过其优化过程自动发现最优模型多样性。在视觉、自然语言处理和现实世界医学影像数据集上的实验表明，FedFew，仅使用3个模型，一致优于其他最先进的方法。代码可在https://github.com/pgg3/FedFew上获得。
英文摘要：
Personalized Federated Learning (PFL) aims to train customized models for clients with highly heterogeneous data distributions while preserving data privacy. Existing approaches often rely on heuristics like clustering or model interpolation, which lack principled mechanisms for balancing heterogeneous client objectives. Serving $M$ clients with distinct data distributions is inherently a multi-objective optimization problem, where achieving optimal personalization ideally requires $M$ distinct models on the Pareto front. However, maintaining $M$ separate models poses significant scalability challenges in federated settings with hundreds or thousands of clients. To address this challenge, we reformulate PFL as a few-for-many optimization problem that maintains only $K$ shared server models ($K \ll M$) to collectively serve all $M$ clients. We prove that this framework achieves near-optimal personalization: the approximation error diminishes as $K$ increases and each client's model converges to each client's optimum as data grows. Building on this reformulation, we propose FedFew, a practical algorithm that jointly optimizes the $K$ server models through efficient gradient-based updates. Unlike clustering-based approaches that require manual client partitioning or interpolation-based methods that demand careful hyperparameter tuning, FedFew automatically discovers the optimal model diversity through its optimization process. Experiments across vision, NLP, and real-world medical imaging datasets demonstrate that FedFew, with just 3 models, consistently outperforms other state-of-the-art approaches. Code is available at https://github.com/pgg3/FedFew.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
