Efficient and Robust Regularized Federated Recommendation

TL;DR

This paper introduces RFRec and RFRecF, novel federated recommendation methods that reformulate the problem as convex optimization, ensuring convergence, efficiency, robustness, and privacy protection, with strong empirical results on benchmark datasets.

Contribution

It reformulates federated recommendation as a convex optimization problem and proposes two efficient algorithms, RFRec and RFRecF, improving convergence, communication efficiency, and privacy.

Findings

RFRec and RFRecF outperform baselines on benchmark datasets.

The methods enhance communication efficiency and robustness.

Theoretical guarantees support convergence and privacy protection.

Abstract

Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predominantly used raises serious privacy concerns. The federated recommender system (FedRS) addresses this by updating models on clients, while a central server orchestrates training without accessing private data. Existing FedRS approaches, however, face unresolved challenges, including non-convex optimization, vulnerability, potential privacy leakage risk, and communication inefficiency. This paper addresses these challenges by reformulating the federated recommendation problem as a convex optimization issue, ensuring convergence to the global optimum. Based on this, we devise a novel method, RFRec, to tackle this optimization problem efficiently. In addition, we propose RFRecF, a highly efficient version that incorporates non-uniform stochastic gradient descent to improve communication efficiency. In user preference modeling, both methods learn local and global models, collaboratively learning users' common and personalized interests under the federated learning setting. Moreover, both methods significantly enhance communication efficiency, robustness, and privacy protection, with theoretical support. Comprehensive evaluations on four benchmark datasets demonstrate RFRec and RFRecF's superior performance compared to diverse baselines.