基于一致性优化的集群联邦学习中的群体共识达成机制（英文版）

英文标题：FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization中文摘要：本论文介绍了一个解决群集联合学习问题的新方法，该方法基于共识优化 (CBO) 中的思想，使用相互作用的粒子系统进行全局优化，经过数学推导得到了该模型的收敛性证明，并通过实验结果证实了 FedCBO 算法的有效性。英文摘要：Federated learning is an important framework in modern machine learning thatseeks to integrate the training of learning models from multiple users, eachuser having their own local data set, in a way that is sensitive to dataprivacy and to communication loss constraints. In clu