标题： FedCBO：通过基于共识的优化在分簇联邦学习中达成群体共识 显示英文标题

标题： FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization

摘要： 联邦学习是现代机器学习中的一个重要框架，旨在以一种对数据隐私和通信损失约束敏感的方式，整合来自多个用户的模型训练，每个用户都有自己的本地数据集。 在聚类联邦学习中，假设用户之间存在一个额外的未知组结构，目标是为每个组训练有用的模型，而不是为所有用户训练一个单一的全局模型。 在本文中，我们提出了一种受基于共识优化（CBO）思想启发的聚类联邦学习问题的新解决方案。 我们的新型CBO方法基于一个对组成员身份无感知的相互作用粒子系统。 我们的模型是基于严格的数学推理，包括描述我们粒子系统大量粒子极限的平均场分析，以及在平均场条件下对一般非凸目标函数（对应于每个用户组的损失函数）的同时全局优化的收敛保证。 实验结果展示了我们的FedCBO算法与其他最先进的方法相比的有效性，并有助于验证我们的方法论和理论工作。 显示英文摘要

摘要： Federated learning is an important framework in modern machine learning that seeks to integrate the training of learning models from multiple users, each user having their own local data set, in a way that is sensitive to data privacy and to communication loss constraints. In clustered federated learning, one assumes an additional unknown group structure among users, and the goal is to train models that are useful for each group, rather than simply training a single global model for all users. In this paper, we propose a novel solution to the problem of clustered federated learning that is inspired by ideas in consensus-based optimization (CBO). Our new CBO-type method is based on a system of interacting particles that is oblivious to group memberships. Our model is motivated by rigorous mathematical reasoning, including a mean field analysis describing the large number of particles limit of our particle system, as well as convergence guarantees for the simultaneous global optimization of general non-convex objective functions (corresponding to the loss functions of each cluster of users) in the mean-field regime. Experimental results demonstrate the efficacy of our FedCBO algorithm compared to other state-of-the-art methods and help validate our methodological and theoretical work.