Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders

TL;DR

This paper introduces PersonalFR, a federated learning approach for personalized recommender systems that preserves user privacy while achieving high recommendation accuracy, with reduced computation and communication costs.

Contribution

It proposes a novel autoencoder-based federated recommendation model with partial updates, enabling personalization without sharing raw data or full models.

Findings

PersonalFR achieves comparable accuracy to centralized models.

It significantly reduces communication overhead.

It maintains user privacy effectively.

Abstract

Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized autoencoder-based recommendation model with Federated Learning (FL) to address these challenges. PersonalFR guarantees that each user can learn a personal model from the local dataset and other participating users' data without sharing local data, data embeddings, or models. PersonalFR consists of three main components, including AutoEncoder-based RSs (ARSs) that learn the user-item interactions, Partially Federated Learning (PFL) that updates the encoder locally and aggregates the decoder on the server-side, and Partial Compression (PC) that only computes and transmits active model parameters. Extensive experiments on two real-world datasets demonstrate that PersonalFR can achieve private and personalized performance comparable to that trained by centralizing all users' data. Moreover, PersonalFR requires significantly less computation and communication overhead than standard FL baselines.