Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets.

翻译：在联合学习(FL)中,客户之间的差异通常会妨碍客户知识汇总在梯度空间时的优化趋同和概括性业绩。例如,客户在数据分布、网络延缓度、输入/产出空间和/或模型结构方面可能有所不同,这很容易导致当地梯度的错配。为了改善对异性容忍度,我们提议了一个新型的Federal 原型学习(FedProto)框架,客户和服务器在其中传送抽象的等级原型,而不是梯度。FedProto综合了从不同客户收集的当地原型,然后将全球原型发送给所有客户,以便对当地模型进行培训。关于每个客户的培训旨在尽可能减少当地数据的分类错误,同时使由此产生的本地原型与相应的全球原型保持足够接近。此外,我们对FedProto在非convelx目标下的趋同率进行理论分析。在实验中,我们提议为异性FedProto设定一个基准,在多个数据集上比最近几个FL方法。