Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
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Abstract (arXiv)
We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution.
Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2020.
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Citing papers per year: 2021: 19; 2022: 15; 2023: 15; 2024: 11; 2025: 7; 2026: 4.
71 papers.
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50 most cited of 71 All 71 (JSON)
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- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture24 Mar 2025
- 2025Biased Federated Learning under Wireless Heterogeneity8 Mar 2025
- 2025The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence6 Mar 2025
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- 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing29 May 2024
- 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systems3 May 2024
- 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks23 Apr 2024
- 2024Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach28 Feb 2024
- 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach1 Feb 2024
- 2024Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method30 Jan 2024
- 2024Joint Probability Selection and Power Allocation for Federated Learning15 Jan 2024
- 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning30 Nov 2023
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selection28 Nov 2023
- 2023Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network16 Nov 2023
- 2023Personalized Federated Learning via ADMM with Moreau Envelope12 Nov 2023
- 2023SoK: Memorisation in machine learning6 Nov 2023
- 2023Over-the-Air Federated Learning and Optimization16 Oct 2023
- 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices24 Sep 2023
- 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning26 May 2023
- 2023Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization22 May 2023
- 2023Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training4 May 2023
- 2023Dynamic Scheduling for Federated Edge Learning with Streaming Data2 May 2023
- 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services13 Apr 2023
- 2023Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks9 Mar 2023
- 2023Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning27 Jan 2023
- 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networks3 Jan 2023
- 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection22 Dec 2022
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks14 Dec 2022
- 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO13 Dec 2022
- 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles7 Dec 2022
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunities3 Nov 2022
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2022To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing5 Sep 2022
- 2022CFLIT: Coexisting Federated Learning and Information Transfer26 Jul 2022
- 2022Over-the-Air Federated Edge Learning with Hierarchical Clustering19 Jul 2022
- 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks6 Jun 2022
- 2022Semi-Decentralized Federated Learning with Collaborative Relaying23 May 2022
- 2022Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing23 Mar 2022
- 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design11 Mar 2022
50 newest of 71 All 71 (JSON)
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
- 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
- 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
- 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
- 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
- 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
- 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
- 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
12 most cited of 71 All 71 (JSON)
- 2026Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning30 Sep 2026
- 2026Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning14 Sep 2026
- 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning14 Jul 2026
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning11 Mar 2026
- 2025ProbSelect: Stochastic Client Selection for GPU-Accelerated Compute Devices in the 3D Continuum11 Nov 2025
- 2025CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks24 Sep 2025
- 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning9 Sep 2025
- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
- 2025Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture24 Mar 2025
- 2025Biased Federated Learning under Wireless Heterogeneity8 Mar 2025
- 2025The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence6 Mar 2025
- 2024Distributed Online Optimization with Stochastic Agent Availability25 Nov 2024
12 newest of 71 All 71 (JSON)