FedProc: Prototypical Contrastive Federated Learning on Non-IID data
This addresses the challenge of efficient collaborative learning for clients with non-IID data in federated learning, representing an incremental improvement.
The paper tackles the problem of training deep learning models on non-IID data in federated learning by proposing FedProc, a framework that uses prototypes as global knowledge to correct local training, resulting in accuracy improvements of 1.6% to 7.9% compared to state-of-the-art methods.
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.