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.

翻译：联邦学习（FL）是一种新兴技术，能够在保护隐私的同时训练海量地理分布式边缘数据。然而，由于边缘设备日益增长的异构性，联邦学习在公平性和计算效率方面面临固有挑战，导致近期最先进解决方案的性能通常不尽如人意。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联邦学习的异构性。具体而言，CFL为每个客户端从专门设计的全局模型中定制个性化模型，并联合在线训练模型搜索辅助器与新型聚合算法进行协同指导。大量实验表明，CFL在联邦学习训练和边缘推理方面具有全栈优势，显著提升了最先进技术在模型精度（非异构环境提升至7.2%，异构环境提升至21.8%）、效率及联邦学习公平性方面的表现。