Scheduling Policies for Federated Learning in Wireless Networks
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Abstract (arXiv)
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.
Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2019.
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Citing papers per year: 2021: 45; 2022: 29; 2023: 24; 2024: 20; 2025: 11; 2026: 2.
131 papers.
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- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
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- 2025A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks5 Sep 2025
- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
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- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture24 Mar 2025
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- 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach21 Jun 2024
- 2024Federated Learning with Multi-resolution Model Broadcast30 May 2024
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- 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks23 Apr 2024
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- 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading23 Dec 2023
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- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selection28 Nov 2023
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- 2023Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks7 Nov 2023
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- 2023RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning1 Jun 2023
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference10 May 2023
- 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy9 Apr 2023
50 newest of 131 All 131 (JSON)
- 2021Federated Learning for Smart Healthcare: A Surveycited by 184
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
- 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
- 2021Federated Learning: A Signal Processing Perspectivecited by 49
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
- 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
- 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 25
12 most cited of 130 All 130 (JSON)
- 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation14 Aug 2026
- 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation27 Apr 2026
- 2025Timely Parameter Updating in Over-the-Air Federated Learning22 Dec 2025
- 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off30 Oct 2025
- 2025A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks5 Sep 2025
- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
- 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification8 Jun 2025
- 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning6 May 2025
- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture24 Mar 2025
- 2025Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data20 Mar 2025
- 2025Biased Federated Learning under Wireless Heterogeneity8 Mar 2025
- 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization1 Mar 2025
12 newest of 130 All 130 (JSON)