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 results 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 by 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.

翻译：联邦学习（FL）是一种新兴技术，能够在维护隐私的同时对大规模地理分布的边缘数据进行训练。然而，由于边缘设备异质性不断增加，FL在公平性和计算效率方面存在固有挑战，导致现有最优（SOTA）方案通常表现出次优性能。本文提出一种定制化联邦学习（CFL）系统，旨在从多个维度消除FL异质性。具体而言，CFL通过在线训练的模型搜索辅助器与新型聚合算法联合指导，为每个客户端定制由特殊设计的全局模型衍生的个性化模型。大量实验表明，CFL在FL训练与边缘推理方面均具有全栈优势，并在模型精度（非异质环境下提升高达7.2%，异质环境下提升高达21.8%）、效率及FL公平性方面显著改善SOTA性能。