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Federated learning (FL) is an emerging technique that trains massive andgeographically distributed edge data while maintaining privacy. However, FL hasinherent challenges in terms of fairness and computational efficiency due tothe rising heterogeneity of edges, and thus usually results in sub-optimalperformance in recent state-of-the-art (SOTA) solutions. In this paper, wepropose a Customized Federated Learning (CFL) system to eliminate FLheterogeneity from multiple dimensions. Specifically, CFL tailors personalizedmodels from the specially designed global model for each client jointly guidedby an online trained model-search helper and a novel aggregation algorithm.Extensive experiments demonstrate that CFL has full-stack advantages for bothFL training and edge reasoning and significantly improves the SOTA performancew.r.t. model accuracy (up to 7.2% in the non-heterogeneous environment and upto 21.8% in the heterogeneous environment), efficiency, and FL fairness.

Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models