通过类别感知的子网融合，提升联邦学习个性化效果
FedSub: Introducing Class-aware Subnetworks Fusion to Enhance Personalized Federated Learning
- 用数据原型捕捉客户端类别相似性，分组后融合子网
- 在高异构数据下实现快速收敛与高分类准确率
- 适合医疗、运动识别等个性化需求强的应用
个性化联邦学习旨在解决协作训练中的非独立同分布（non-IID）数据挑战。现有方法难以平衡个性化与泛化能力，常过度简化客户端相似性或过度依赖全局模型。本文提出FedSub，一种基于数据原型和模型子网融合的类别感知更新方法。服务器对各客户端每类数据的原型进行聚类，以捕捉标签相关的客户端相似性；同时，每个类别对应的模型子网被提取并根据聚类结果融合，生成细粒度、类别特异且高度个性化的模型更新。在人体活动识别与移动健康应用三个真实场景中，面对高数据异构性，实验表明FedSub相较现有方法具备更快收敛速度与更高分类性能。
原文摘要 · Abstract (English)
Personalized Federated Learning aims at addressing the challenges of non-IID data in collaborative model training. However, existing methods struggle to balance personalization and generalization, often oversimplifying client similarities or relying too heavily on global models. In this paper, we propose FedSub, a novel approach that introduces class-aware model updates based on data prototypes and model subnetworks fusion to enhance personalization. Prototypes serve as compact representations of client data for each class, clustered on the server to capture label-specific similarities among the clients. Meanwhile, model subnetworks encapsulate the most relevant components to process each class and they are then fused on the server based on the identified clusters to generate fine-grained, class-specific, and highly personalized model updates for each client. Experimental results in three real-world scenarios with high data heterogeneity in human activity recognition and mobile health applications demonstrate the effectiveness of FedSub with respect to state-of-the-art methods to achieve fast convergence and high classification performance.
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