Federated learning allows multiple clients to collaborate to train
high-performance deep learning models while keeping the training data locally.
However, when the local data of all clients are not independent and identically
distributed (i.e., non-IID), it is challenging to implement this form of
efficient collaborative learning. Although significant efforts have been
dedicated to addressing this challenge, the effect on the image classification
task is still not satisfactory. In this paper, we propose FedProc: prototypical
contrastive federated learning, which is a simple and effective federated
learning framework. The key idea is to utilize the prototypes as global
knowledge to correct the local training of each client. We design a local
network architecture and a global prototypical contrastive loss to regulate the
training of local models, which makes local objectives consistent with the
global optima. Eventually, the converged global model obtains a good
performance on non-IID data. Experimental results show that, compared to
state-of-the-art federated learning methods, FedProc improves the accuracy by
$1.6\%\sim7.9\%$ with acceptable computation cost.

该研究提出了 FedProc，即原型对比联邦学习，一种简单有效的联邦学习框架，通过利用原型作为全局知识来纠正每个客户的本地训练，使用本地网络架构和全局对比损失来规范本地模型的训练，从而最终获得在非 I.I.D 数据上良好的性能。