A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
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Abstract (arXiv)
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.
Key facts. Cited by 262 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2019.
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Citing papers per year: 2021: 61; 2022: 58; 2023: 53; 2024: 41; 2025: 35; 2026: 14.
262 papers.
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50 most cited of 262 All 262 (JSON)
- 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis14 Sep 2026
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- 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation14 Aug 2026
- 2026Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning22 Jul 2026
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- 2026Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning22 Jun 2026
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- 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification8 Jun 2025
- 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling3 Jun 2025
- 2025SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks26 May 2025
- 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning16 May 2025
- 2025Energy-Efficient Federated Learning for AIoT using Clustering Methods14 May 2025
- 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning6 May 2025
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- 2025A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning12 Mar 2025
- 2025Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing12 Mar 2025
- 2025Biased Federated Learning under Wireless Heterogeneity8 Mar 2025
- 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization1 Mar 2025
- 2025Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach24 Feb 2025
- 2025Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks7 Feb 2025
- 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks20 Jan 2025
- 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data16 Jan 2025
- 2024Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices28 Dec 2024
50 newest of 262 All 262 (JSON)
- 2021A Field Guide to Federated Optimizationcited by 312
- 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
- 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
- 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
- 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
- 2021Federated Learning: A Signal Processing Perspectivecited by 49
- 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
- 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
12 most cited of 258 All 258 (JSON)
- 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis14 Sep 2026
- 2026Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data13 Sep 2026
- 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation14 Aug 2026
- 2026Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning22 Jul 2026
- 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning14 Jul 2026
- 2026Distributed Deep Variational Approach for Privacy-preserving Data Release4 May 2026
- 2026Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation25 Mar 2026
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning11 Mar 2026
- 2026A Secure and Private Distributed Bayesian Federated Learning Design23 Feb 2026
- 2026ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning19 Feb 2026
- 2026Decentralized Federated Learning With Energy Harvesting Devices15 Feb 2026
- 2026VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI3 Feb 2026
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