A Review of Resource Allocation Strategies for Federated Learning in Wireless Networks

Abstract

The rapid advances in wireless communication technologies have driven the adoption of distributed artificial intelligence in next-generation wireless networks. However, traditional centralized learning approaches become increasingly inefficient due to limited communication bandwidth, stringent latency constraints, and growing concerns regarding data privacy and security. Federated learning (FL) has emerged as a promising distributed paradigm that enables collaborative model training across edge devices without sharing raw data. Despite these advantages, its deployment in wireless networks remains challenging due to the scarcity of communication resources and the complexity of managing the heterogeneous devices. To date, however, resource allocation strategies in FL have not been extensively investigated from a systematic optimization perspective, while resource-aware system design largely underexplored. To bridge this gap, this survey provides a comprehensive and structured review of resource allocation in wireless FL systems. A unified analytical framework is introduced to characterize resource allocation strategies along three key dimensions: decision variable design, objective function formulation with associated performance metrics, and solution methodologies. Resource allocation strategies are further categorized to reflect increasing multi-resource demands. It highlights the importance of joint optimization and its underlying mathematical coupling in enhancing system efficiency and learning performance. Mathematical reformulation patterns are synthesized to explain common approaches for transforming complex and non-convex resource allocation problems into more tractable forms for optimization. A comparative and critical analysis of major solution paradigms is presented, encompassing classical optimization, metaheuristic algorithms, reinforcement learning, game-theoretic frameworks, and hybrid methods. Finally, this survey identifies key research gaps and promising future directions for integrating FL into next-generation wireless networks, including 5G/6G systems.