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Recommender Systems (RSs) have become increasingly important in manyapplication domains, such as digital marketing. Conventional RSs often need tocollect users' data, centralize them on the server-side, and form a globalmodel to generate reliable recommendations. However, they suffer from twocritical limitations: the personalization problem that the RSs trainedtraditionally may not be customized for individual users, and the privacyproblem that directly sharing user data is not encouraged. We proposePersonalized Federated Recommender Systems (PersonalFR), which introduces apersonalized autoencoder-based recommendation model with Federated Learning(FL) to address these challenges. PersonalFR guarantees that each user canlearn a personal model from the local dataset and other participating users'data without sharing local data, data embeddings, or models. PersonalFRconsists of three main components, including AutoEncoder-based RSs (ARSs) thatlearn the user-item interactions, Partially Federated Learning (PFL) thatupdates the encoder locally and aggregates the decoder on the server-side, andPartial Compression (PC) that only computes and transmits active modelparameters. Extensive experiments on two real-world datasets demonstrate thatPersonalFR can achieve private and personalized performance comparable to thattrained by centralizing all users' data. Moreover, PersonalFR requiressignificantly less computation and communication overhead than standard FLbaselines.

Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders