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,在维护隐私的同时,培训大规模和地理分布的边缘数据,但是,FL在公平性和计算效率方面有内在挑战,因为边缘的异质性正在上升,因此通常导致最近的最先进的(SOTA)解决方案表现不尽如人意。在本文中,我们建议采用自定义的联邦学习(CFL)系统,从多个层面消除FL的异质性。具体来说,CFL裁缝从专门为每个客户设计的全球模型中定制个性化模型,联合指导了一个经过在线培训的模型搜索助手和新颖的汇总算法。广泛的实验表明,CFL具有对FL培训和边缘推理的充分优势,大大改善了SOTA的性能(在非异质环境中达到7.2%,在多种环境达到21.8%)、效率和FL公平性模型的精确度。