Federated learning (FL) allows a large number of clients to improve their respective models through training a shared global model. However, passing the same global model is not conducive to the training of a few clients and leads to a large loss of localization information. In practical, there are often some prior information that can be shared between clients. Our study takes into account the use of such prior information to calculate a personalized global model for each client, resulting in an enhanced personalized federated learning framework, dubbed PerFL for short, that takes advantage of available client features that can be shared with other clients. More specifically, PerFL calculates the incidence matrix of all involved clients by using the permitted shareable side information and then updates the local models by using their similar clients instead of all clients. Employing the neural network as the classification model, PerFL learns the parameter matrices at each client in an iterative manner. On three publicly available image datasets, PerFL can benefit from the employed similarity and achieve an improved classification performance in comparison with the state-of-the-art FL models.

This paper studies the problem of developing contrastive learning into the privacy-protected federated learning (FL), which is to achieve more data samples for model training. The existing methods usually encourage the global model and local models in FL to be the same one, often ignoring the data heterogeneity of the clients. In this paper, we proposed a method of personalized federated contrastive learning to improve the FL model performance for each client’s task, by learning a global representation and a local representation simultaneously. Our method is a novel FL framework that borrows the scheme of contrastive learning (CL), where one CL branch is the global model while the other branch is the local model divided into a share part and a personalized part. The proposed model is then trained by maximizing the agreement between the global model and the sharing part of the local model and meanwhile minimizing the agreement between the global model and the personalized part. We conducted evaluations on three public datasets for federated image classification. The results show that the proposed method can benefit from the personalization of local models and thus achieve better accuracy in comparison with the state-of-the-art FL models.