解决联邦原型学习中特征保真与区分度矛盾问题
FedDBP: Enhancing Federated Prototype Learning with Dual-Branch Features and Personalized Global Fusion
- 客户端双分支投影器同步使用L2对齐与对比学习
- 服务器端基于Fisher信息识别重要通道实现个性化融合
- 在10个基准方法上表现更优，提升异构联邦学习效果
联邦原型学习（FPL）作为应对异构联邦学习（HFL）的方案，有效缓解了数据与模型异构带来的挑战。然而，现有FPL方法难以平衡特征的保真性与区分性，且受限于单一全局原型。本文提出FedDBP，一种新型FPL方法：客户端设计双分支特征投影器，同时采用L2对齐与对比学习，保障本地特征的保真性与区分性；服务器端引入基于Fisher信息的个性化全局原型融合策略，识别本地原型的重要通道。大量实验表明，FedDBP在10种先进方法中表现更优，显著提升异构联邦学习性能。
原文摘要 · Abstract (English)
Federated prototype learning (FPL), as a solution to heterogeneous federated learning (HFL), effectively alleviates the challenges of data and model heterogeneity.However, existing FPL methods fail to balance the fidelity and discriminability of the feature, and are limited by a single global prototype. In this paper, we propose FedDBP, a novel FPL method to address the above issues. On the client-side, we design a Dual-Branch feature projector that employs L2 alignment and contrastive learning simultaneously, thereby ensuring both the fidelity and discriminability of local features. On the server-side, we introduce a Personalized global prototype fusion approach that leverages Fisher information to identify the important channels of local prototypes. Extensive experiments demonstrate the superiority of FedDBP over ten existing advanced methods.
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