FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

Understand

Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical.

• Existing methods usually focus on centralized data, where abundant and high-quality negative samples are easy to obtain.
• However, centralized user data storage and exploitation may lead to privacy risks and concerns, while decentralized user data on a single client can be too sparse and biased for accurate contrastive learning.
• In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected.