FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

• URL: http://arxiv.org/abs/2204.09850v1
• Date: Thu, 21 Apr 2022 02:37:10 GMT
• Title: FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
• Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
• Abstract summary: FedCL can exploit high-quality negative samples for effective model training with privacy well protected. We first infer user embeddings from local user data through the local model on each client, and then perturb them with local differential privacy (LDP) Since individual user embedding contains heavy noise due to LDP, we propose to cluster user embeddings on the server to mitigate the influence of noise.
• Score: 98.5705258907774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We first infer user embeddings from local user data through the local model on each client, and then perturb them with local differential privacy (LDP) before sending them to a central server for hard negative sampling. Since individual user embedding contains heavy noise due to LDP, we propose to cluster user embeddings on the server to mitigate the influence of noise, and the cluster centroids are used to retrieve hard negative samples from the item pool. These hard negative samples are delivered to user clients and mixed with the observed negative samples from local data as well as in-batch negatives constructed from positive samples for federated model training. Extensive experiments on four benchmark datasets show FedCL can empower various recommendation methods in a privacy-preserving way.

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