mlsanityData to 14 Oct 2026

Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning

arXiv 2505.03533 ↗First posted 6 May 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Judicious resource allocation can effectively enhance federated learning (FL) training performance in wireless networks by addressing both system and statistical heterogeneity. However, existing strategies typically rely on block fading assumptions, which overlooks rapid channel fluctuations within each round of FL gradient uploading, leading to a degradation in FL training performance. Therefore, this paper proposes a small-scale-fading-aware resource allocation strategy using a multi-agent reinforcement learning (MARL) framework. Specifically, we establish a one-step convergence bound of the FL algorithm and formulate the resource allocation problem as a decentralized partially observable Markov decision process (Dec-POMDP), which is subsequently solved using the QMIX algorithm. In our framework, each client serves as an agent that dynamically determines spectrum and power allocations within each coherence time slot, based on local observations and a reward derived from the convergence analysis. The MARL setting reduces the dimensionality of the action space and facilitates decentralized decision-making, enhancing the scalability and practicality of the solution. Experimental results demonstrate that our QMIX-based resource allocation strategy significantly outperforms baseline methods across various degrees of statistical heterogeneity. Additionally, ablation studies validate the critical importance of incorporating small-scale fading dynamics, highlighting its role in optimizing FL performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 6 May 2025.

Builds on

23 of 40 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2016An overview of gradient descent optimization algorithmscited by 1,033
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  7. 2019Towards Federated Learning at Scale: System Designcited by 832
  8. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  9. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  11. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  12. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 184
11 more
  1. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  2. 2017Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learningcited by 117
  3. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  4. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  5. 2018Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networkscited by 56
  6. 2021FedLab: A Flexible Federated Learning Frameworkcited by 55
  7. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 31
  8. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  9. 2020Federated Learning and Wireless Communicationscited by 27
  10. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  11. 2019Learn to Compress CSI and Allocate Resources in Vehicular Networkscited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Adaptive Biased User Scheduling for Heterogeneous Wireless Federate Learning Network
  2. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
  3. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
  4. 2024Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
  5. 2025QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management
  6. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
  7. 2025FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers ↗
  8. 2025Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning