Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions. Existing works offer personalization primarily to clients who participate in the FL process, making it hard to encompass new clients who were absent or newly show up. In this paper, we propose FedBasis, a novel PFL framework to tackle such a deficiency. FedBasis learns a set of few shareable ``basis'' models, which can be linearly combined to form personalized models for clients. Specifically for a new client, only a small set of combination coefficients, not the model weights, needs to be learned. This notion makes FedBasis more parameter-efficient, robust, and accurate than competitive PFL baselines, especially in the low data regime, without increasing the inference cost. To demonstrate the effectiveness and applicability of FedBasis, we also present a more practical PFL testbed for image classification, featuring larger data discrepancies across clients in both the image and label spaces as well as more faithful training and test splits.

翻译：个性化联邦学习（PFL）旨在利用客户端数据的集体智慧，同时构建适应各客户端数据分布的个性化模型。现有工作主要面向参与联邦学习过程的客户端提供个性化服务，难以覆盖未参与或新出现的客户端。本文提出一种新型PFL框架FedBasis以解决该缺陷。FedBasis学习一组少量可共享的"基底"模型，通过线性组合可为客户端生成个性化模型。特别地，对于新客户端，仅需学习少量组合系数而非模型权重。这一特性使FedBasis在参数效率、鲁棒性和准确性方面优于竞争性PFL基准方法（尤其在低数据量场景下），且不增加推理成本。为验证FedBasis的有效性与适用性，我们还构建了更贴近实际的PFL图像分类测试平台，该平台在图像空间与标签空间中均包含更大的客户端数据差异度，并采用更严谨的训练/测试划分方案。