Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
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Abstract (arXiv)
This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data. In such wireless federated learning networks (WFLNs), optimizing the learning performance depends crucially on how clients are selected and how bandwidth is allocated among the selected clients in every learning round, as both radio and client energy resources are limited. While existing works have made some attempts to allocate the limited wireless resources to optimize FL, they focus on the problem in individual learning rounds, overlooking an inherent yet critical feature of federated learning. This paper brings a new long-term perspective to resource allocation in WFLNs, realizing that learning rounds are not only temporally interdependent but also have varying significance towards the final learning outcome. To this end, we first design data-driven experiments to show that different temporal client selection patterns lead to considerably different learning performance. With the obtained insights, we formulate a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints, and develop a new algorithm that utilizes only currently available wireless channel information but can achieve long-term performance guarantee. Further experiments show that our algorithm results in the desired temporal client selection pattern, is adaptive to changing network environments and far outperforms benchmarks that ignore the long-term effect of FL.
Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2020.
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Citing papers per year: 2021: 14; 2022: 15; 2023: 31; 2024: 18; 2025: 17; 2026: 7.
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38 more
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- 2024Federated Learning With Energy Harvesting Devices: An MDP Framework17 May 2024
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- 2023Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection31 Dec 2023
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- 2023Green Edge AI: A Contemporary Survey1 Dec 2023
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selection28 Nov 2023
- 2023Scheduling and Communication Schemes for Decentralized Federated Learning27 Nov 2023
- 2023Personalized Federated Learning via ADMM with Moreau Envelope12 Nov 2023
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- 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices24 Sep 2023
50 newest of 102 All 102 (JSON)
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
- 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
- 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
- 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2023FwdLLM: Efficient FedLLM using Forward Gradientcited by 25
- 2022FedAdapter: Efficient Federated Learning for Modern NLPcited by 24
- 2023Green Edge AI: A Contemporary Surveycited by 24
- 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
- 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
12 most cited of 101 All 101 (JSON)
- 2026Adaptive Determinantal Client Scheduling in Federated Learning20 Sep 2026
- 2026Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data2 Jul 2026
- 2026EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning5 Jun 2026
- 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks29 May 2026
- 2026Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection15 May 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025Communication-Efficient Federated Learning with Adaptive Number of Participants19 Aug 2025
- 2025Communication and Computation Efficient Split Federated Learning in O-RAN4 Aug 2025
- 2025FedABC: Attention-Based Client Selection for Federated Learning with Long-Term View28 Jul 2025
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity21 Jul 2025
- 2025Lightweight Federated Learning over Wireless Edge Networks13 Jul 2025
- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
12 newest of 101 All 101 (JSON)