Dual-Layer Bidirectional Contrastive Federated Learning: Enhancing Feature Extraction and Personalization Simultaneously

As a privacy-preserving machine learning paradigm, federated learning enables collaborative training across multiple clients without sharing data. However, when there are discrepancies in the data distributions of individual clients, the training accuracy often significantly decreases. Although existing personalized federated learning approaches aim to preserve each client’s capability on their respective datasets, this is still insufficient to fully address the problem. In this paper, we propose a dual-layer bidirectional contrastive federated learning method (FDCL), which enhances the feature extraction ability of the model while maintaining personalization, thereby improving the overall model performance. Specifically, we divide the model into a feature extraction layer and a classification layer. In the feature extraction layer, we reduce the distance between the client and the global model while enlarging the distance from the previous client version, which enhances the model’s feature extraction capability. In the classification layer, we reduce the distance between the client and the previous version while increasing the distance from the global model, which improves the model’s personalization ability. Extensive experiments demonstrate that our method outperforms other competitive methods in multiple image classification tasks.