Federated Contrastive Learning

Federated contrastive learning (FCL) addresses the challenge of training machine learning models on decentralized, private data by combining federated learning with contrastive learning techniques. Current research focuses on adapting contrastive learning algorithms, such as SimCLR and variations thereof, to the federated setting, often incorporating strategies to handle non-identically distributed data and limited labeled data across clients, sometimes leveraging pre-trained models or clustering methods. This approach is significant for enabling collaborative model training while preserving data privacy, with applications ranging from personalized communication to medical image analysis and recommendation systems.