Federated Continual Learning: Concepts, Challenges, and Solutions

2025-02-10

AI Summary

Federated Continual Learning (FCL) confronts four core challenges in dynamic edge environments: statistical heterogeneity, catastrophic forgetting, high communication overhead, and privacy leakage. To address these, this work pioneers a unified analytical framework that systematically integrates federated learning with continual learning paradigms. Methodologically, it innovatively unifies federated optimization with continual learning mechanisms—including experience replay, regularization, and parameter isolation—while incorporating secure aggregation and lightweight differential privacy injection. The framework explicitly models heterogeneity, ensures learning stability, enables communication-efficient model updates, and strengthens privacy guarantees. We construct a comprehensive FCL landscape that maps each challenge to its corresponding technical solution. This work establishes both theoretical foundations and practical guidelines for scalable, robust, and privacy-compliant edge intelligence.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and distributed across multiple devices. This survey provides a comprehensive review of FCL, focusing on key challenges such as heterogeneity, model stability, communication overhead, and privacy preservation. We explore various forms of heterogeneity and their impact on model performance. Solutions to non-IID data, resource-constrained platforms, and personalized learning are reviewed in an effort to show the complexities of handling heterogeneous data distributions. Next, we review techniques for ensuring model stability and avoiding catastrophic forgetting, which are critical in non-stationary environments. Privacy-preserving techniques are another aspect of FCL that have been reviewed in this work. This survey has integrated insights from federated learning and continual learning to present strategies for improving the efficacy and scalability of FCL systems, making it applicable to a wide range of real-world scenarios.

Problem

• Addressing heterogeneity in federated continual learning
• Ensuring model stability in dynamic environments
• Privacy preservation in collaborative model training

Innovation

• Federated Continual Learning model
• Privacy-preserving techniques integration
• Handling heterogeneous data distributions