Federated Reinforcement Learning for Privacy-Preserving Resource Management in Multi-Domain 5G Network Slicing
Keywords:
Federated reinforcement learning; 5G network slicing; privacy‑preserving; multi‑domain resource management; edge‑cloud coordination; quality of service; policy optimizationAbstract
The proliferation of 5G network slicing has enabled operators to instantiate multiple logical networks over a shared physical infrastructure, each tailored to distinct service requirements. However, orchestration of end‑to‑end slices across multiple administrative domains introduces formidable challenges in resource allocation, quality‑of‑service assurance, and data governance. Centralized machine learning approaches that gather sensitive traffic traces, user mobility patterns, and slice state information from participating domains raise significant privacy and regulatory concerns, particularly when data traverse jurisdictions with differing legal frameworks. Federated reinforcement learning offers a promising alternative by allowing domains to collaboratively train resource management policies without sharing raw operational data. This paper provides a comprehensive system‑level analysis of federated reinforcement learning architectures designed for privacy‑preserving multi‑domain 5G network slicing. We examine the structural trade‑offs between fully distributed and hierarchical aggregation models, the interplay between local policy generalization and global coordination, and the privacy‑preserving mechanisms that underpin secure federated optimization. The discussion extends to robustness against non‑identically distributed data, fairness in resource allocation across heterogeneous slice tenants, and the governance frameworks required to sustain trust among competing stakeholders. Deployment considerations encompassing edge‑cloud continuum orchestration, energy efficiency, and interoperability with existing 3GPP management interfaces are critically assessed. Policy implications regarding cross‑border data flow regulations, algorithmic accountability, and the evolution of service level agreements in a federated multi‑domain ecosystem are explored. By synthesizing advances in federated learning, deep reinforcement learning, and 5G slicing standards, the paper articulates a research roadmap for resilient, equitable, and privacy‑compliant resource management in next‑generation networks.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
