Federated learning (FL) is an emerging technique that trains massive and
geographically distributed edge data while maintaining privacy. However, FL has
inherent challenges in terms of fairness and computational efficiency due to
the rising heterogeneity of edges, and thus usually result in sub-optimal
performance in recent state-of-the-art (SOTA) solutions. In this paper, we
propose a Customized Federated Learning (CFL) system to eliminate FL
heterogeneity from multiple dimensions. Specifically, CFL tailors personalized
models from the specially designed global model for each client, jointly guided
an online trained model-search helper and a novel aggregation algorithm.
Extensive experiments demonstrate that CFL has full-stack advantages for both
FL training and edge reasoning and significantly improves the SOTA performance
w.r.t. model accuracy (up to 7.2% in the non-heterogeneous environment and up
to 21.8% in the heterogeneous environment), efficiency, and FL fairness.

本文提出了一种个性化的联邦学习（CFL）系统，通过特殊设计的全局模型为每个客户定制个性化模型，采用在线训练的模型搜索辅助器和新型聚合算法，解决 FL 在多个维度上的异质性问题。实验证明，CFL 在 FL 训练和边缘推断方面具有全栈优势，并显著改进了模型准确性（在非异构环境中高达 7.2％，在异构环境中高达 21.8％）、效率和公平性。