提出双流最小二乘法个性化联邦学习，解决非独立同分布数据挑战
APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
- 双流结构：共享主干流建模全局特征，专属优化流实现个体定制
- 实测精度提升1.10%至15.45%，在多数据集上优于现有方法
- 理论保证异构数据下模型不变性，适合数据分布差异大的场景
个性化联邦学习（PFL）旨在通过协同训练为各客户端提供个性化模型。现有方法常受非独立同分布（non-IID）数据影响，严重制约集体泛化能力，进而削弱个性化效果。本文提出一种基于双流最小二乘的分析型个性化联邦学习（APFL）方法。APFL采用冻结的预训练模型作为特征提取骨干，其后设计双流解析模型，兼顾全局泛化与局部个性化。具体而言，共享主流实现跨客户端的通用建模，专属优化流针对每个客户端进行本地个性化调整。所提方法具备理论上的异构性不变性，即无论其他客户端数据分布如何异构，每个客户端的个性化模型保持一致。在多个数据集上的实验验证了其优越性，相比最先进基线，精度提升至少1.10%至15.45%。
原文摘要 · Abstract (English)
Personalized Federated Learning (PFL) has presented a significant challenge to deliver personalized models to individual clients through collaborative training. Existing PFL methods are often vulnerable to non-IID data, which severely hinders collective generalization and then compromises the subsequent personalization efforts. In this paper, to address this non-IID issue in PFL, we propose an Analytic Personalized Federated Learning (APFL) approach via dual-stream least squares. In our APFL, we use a foundation model as a frozen backbone for feature extraction. Subsequent to the feature extractor, we develop dual-stream analytic models to achieve both collective generalization and individual personalization. Specifically, our APFL incorporates a shared primary stream for global generalization across all clients, and a dedicated refinement stream for local personalization of each individual client. The analytical solutions of our APFL enable its ideal property of heterogeneity invariance, theoretically meaning that each personalized model remains identical regardless of how heterogeneous the data are distributed across all other clients. Empirical results across various datasets also validate the superiority of our APFL over state-of-the-art baselines, with advantages of at least 1.10%-15.45% in accuracy.
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