Federated Learning for Distributed and Privacy-Preserving Networking: A Review of Architectures, Applications, and Open Challenges
Abstract
The increasing deployment of intelligent networking systems has created a growing demand for machine learning models capable of processing network data while preserving privacy and reducing centralized data-transfer requirements. Federated Learning (FL) provides a distributed learning paradigm in which participating devices or network nodes collaboratively train models without directly sharing their raw data. This review examines the application of federated learning in computer and communication networks, with particular emphasis on intrusion detection, traffic classification, resource allocation, routing, edge computing, IoT security, and 5G/6G network optimization. Different federated architectures, aggregation strategies, personalization techniques, and communication-efficient learning mechanisms are analyzed. The paper evaluates the benefits and limitations of FL compared with centralized machine learning approaches. Particular attention is given to non-IID data, client heterogeneity, communication overhead, model poisoning, privacy leakage, resource constraints, and scalability. Emerging combinations of federated learning with blockchain, differential privacy, edge intelligence, and secure aggregation are also examined. The review identifies open research problems and outlines opportunities for developing scalable, robust, and privacy-aware intelligent networking systems.
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