Personalized federated learning (PFL) aims to harness the collective wisdom
of clients' data to build customized 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 models, needs to be learned. This notion makes FedBasis
more parameter-efficient, robust, and accurate compared to other competitive
PFL baselines, especially in the low data regime, without increasing the
inference cost. To demonstrate its applicability, 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.

本文提出了一种新的个性化联邦学习框架 ——FedBasis，它学习一组共享的 “基础” 模型，可以线性组合形成客户定制的模型，从而实现在低数据情况下减少推断成本，同时提高参数效率和鲁棒性。