Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. Under heterogeneous clients, however, FL can fail to produce stable training results. Personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. One approach is to decompose model training into shared representation learning and personalized classifier training. Nonetheless, previous works struggle to navigate the bias-variance trade-off in classifier learning, relying solely on limited local datasets or introducing costly techniques to improve generalization. In this work, we frame representation learning as a generative modeling task, where representations are trained with a classifier based on the global feature distribution. We then propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions. Through extensive computer vision benchmarks, we demonstrate that our method can adjust to complex distribution shifts with significant improvements over current state-of-the-art in data-scarce settings.

本研究解决了传统联邦学习在异构客户端下训练结果不稳定的问题。提出了一种新颖的方法，将表示学习视为生成建模任务，并通过算法pFedFDA有效生成适应本地特征分布的个性化模型。通过广泛的计算机视觉基准测试，证明该方法在数据稀缺环境下显著优于现有最先进技术。

基于特征分布适应的个性化联邦学习