通过多维个性化边缘模型实现更公平、更高效的联邦学习
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
AI总结：
针对边缘异构导致联邦学习公平性与效率不足的问题，本文提出CFL系统，借助在线模型搜索辅助器和新型聚合算法生成多维个性化模型，显著提升准确率、效率与公平性。
摘要：
联邦学习（FL）是一种新兴技术，可在保护隐私的同时训练海量且地理分布的边缘数据。然而，由于边缘设备异构性不断增强，FL在公平性和计算效率方面存在固有挑战，因此近期最先进（SOTA）解决方案通常只能取得次优性能。本文提出一种定制化联邦学习（Customized Federated Learning，CFL）系统，从多个维度消除FL异构性。具体而言，CFL在一个在线训练的模型搜索辅助器和一种新型聚合算法的共同引导下，从专门设计的全局模型中为每个客户端裁剪个性化模型。大量实验表明，CFL在FL训练和边缘推理两方面均具备全栈优势，并显著提升SOTA性能：模型准确率最高提升7.2%（非异构环境）和21.8%（异构环境），同时改善效率与FL公平性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
