Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data. However, heterogeneity of data distribution among clients usually leads to poor model inference. In this paper, a prototype-based federated learning framework is proposed, which can achieve better inference performance with only a few changes to the last global iteration of the typical federated learning process. In the last iteration, the server aggregates the prototypes transmitted from distributed clients and then sends them back to local clients for their respective model inferences. Experiments on two baseline datasets show that our proposal can achieve higher accuracy (at least 1%) and relatively efficient communication than two popular baselines under different heterogeneous settings.

翻译：原型帮助联邦学习：朝更快的收敛方向 联邦学习 (FL) 是一种分布式机器学习技术，多个客户端协作训练共享模型而无需交换原始数据。然而，数据在客户端之间的分布异质性通常会导致模型推理性能不佳。本文提出了一种基于原型的联邦学习框架，该框架在典型联邦学习过程的最后一次全局迭代中只做了少量更改，即在最后一次迭代中，服务器会聚合联邦的原型，并将其发送回本地客户端以进行各自的模型推理。在两个基准数据集上的实验证明，相对于不同的异构设置下的两个流行基准线，我们的提议可以实现更高的准确性（至少1%）和相对高效的通信。