Federated Continual Recommendation

2025-08-06

AI Summary

To address the challenge of simultaneously preserving user privacy and adapting to non-stationary data streams (i.e., dynamically evolving preferences) in recommender systems, this paper proposes FCRec, a Federated Continual Recommendation framework. FCRec is the first to unify federated learning with continual learning paradigms: it introduces a client-side adaptive replay memory mechanism to mitigate catastrophic forgetting, and a server-side item-level temporal averaging aggregation strategy to ensure stable, cross-client knowledge updating over streaming data. Unlike existing approaches, FCRec guarantees that raw user data remains localized—never leaving the client device—while significantly improving long-term recommendation performance. Extensive experiments on multiple dynamic benchmark datasets demonstrate FCRec’s effectiveness in balancing knowledge adaptability and stability under privacy constraints.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The increasing emphasis on privacy in recommendation systems has led to the adoption of Federated Learning (FL) as a privacy-preserving solution, enabling collaborative training without sharing user data. While Federated Recommendation (FedRec) effectively protects privacy, existing methods struggle with non-stationary data streams, failing to maintain consistent recommendation quality over time. On the other hand, Continual Learning Recommendation (CLRec) methods address evolving user preferences but typically assume centralized data access, making them incompatible with FL constraints. To bridge this gap, we introduce Federated Continual Recommendation (FCRec), a novel task that integrates FedRec and CLRec, requiring models to learn from streaming data while preserving privacy. As a solution, we propose F3CRec, a framework designed to balance knowledge retention and adaptation under the strict constraints of FCRec. F3CRec introduces two key components: Adaptive Replay Memory on the client side, which selectively retains past preferences based on user-specific shifts, and Item-wise Temporal Mean on the server side, which integrates new knowledge while preserving prior information. Extensive experiments demonstrate that F3CRec outperforms existing approaches in maintaining recommendation quality over time in a federated environment.

Problem

• Integrate federated learning with continual recommendation for privacy
• Address non-stationary data streams in federated recommendation systems
• Balance knowledge retention and adaptation under privacy constraints

Innovation

• Federated Continual Recommendation (FCRec) integration
• Adaptive Replay memory for client-side retention
• Item-wise temporal mean for server-side integration