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News recommendation is important for personalized online news services. Mostexisting news recommendation methods rely on centrally stored user behaviordata to both train models offline and provide online recommendation services.However, user data is usually highly privacy-sensitive, and centrally storingthem may raise privacy concerns and risks. In this paper, we propose a unifiednews recommendation framework, which can utilize user data locally stored inuser clients to train models and serve users in a privacy-preserving way.Following a widely used paradigm in real-world recommender systems, ourframework contains two stages. The first one is for candidate news generation(i.e., recall) and the second one is for candidate news ranking (i.e.,ranking). At the recall stage, each client locally learns multiple interestrepresentations from clicked news to comprehensively model user interests.These representations are uploaded to the server to recall candidate news froma large news pool, which are further distributed to the user client at theranking stage for personalized news display. In addition, we propose aninterest decomposer-aggregator method with perturbation noise to better protectprivate user information encoded in user interest representations. Besides, wecollaboratively train both recall and ranking models on the data decentralizedin a large number of user clients in a privacy-preserving way. Experiments ontwo real-world news datasets show that our method can outperform baselinemethods and effectively protect user privacy.

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving