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：联邦学习（FL）是一种分布式机器学习技术，其中多个客户端在不交换原始数据的情况下协同训练共享模型。然而，客户端间数据分布的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，该框架仅需对典型联邦学习过程的最后一次全局迭代进行少量修改即可实现更优的推理性能。在最后一次迭代中，服务器聚合来自分布式客户端传输的原型，然后将其送回本地客户端以供各自模型推理。在两个基准数据集上的实验表明，与两种主流基线方法相比，我们的方案能够在不同异质性设置下实现更高的准确率（至少提升1%）和相对高效的通信。