Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Not on the map. Why?
Abstract (arXiv)
In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.
Key facts. Cited by 90 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 16; 2022: 20; 2023: 16; 2024: 18; 2025: 16; 2026: 4.
90 papers.
Sort:
- 2021Federated Learning for Smart Healthcare: A Surveycited by 182
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
- 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
- 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
38 more
- 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited 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
- 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
- 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
- 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
- 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
- 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
- 2021Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Datacited by 12
- 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12
- 2022Federated Learning with Flexible Controlcited by 12
- 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
- 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
- 2022Time-triggered Federated Learning over Wireless Networkscited by 10
- 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
- 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
- 2023Over-the-Air Federated Learning and Optimizationcited by 10
- 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
- 2023Delay-Aware Hierarchical Federated Learningcited by 9
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
- 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
- 2021A Hybrid Architecture for Federated and Centralized Learningcited by 8
- 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
- 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehiclescited by 8
- 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
- 2021Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networkscited by 7
- 2022Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learningcited by 7
- 2023Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networkscited by 7
- 2021Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trendscited by 6
- 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
- 2022Semi-Synchronous Personalized Federated Learning over Mobile Edge Networkscited by 5
- 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
- 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approachcited by 5
- 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4
- 2023Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labelingcited by 4
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
- 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
50 most cited of 90 All 90 (JSON)
- 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning14 Jul 2026
- 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks29 May 2026
- 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning25 May 2026
- 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation27 Apr 2026
- 2025TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learning6 Nov 2025
- 2025CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks24 Sep 2025
- 2025Hierarchical Federated Learning for Social Network with Mobility18 Sep 2025
- 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning9 Sep 2025
- 2025A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks5 Sep 2025
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity21 Jul 2025
- 2025Efficient Federated Learning with Timely Update Dissemination8 Jul 2025
- 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems10 Jun 2025
38 more
- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
- 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning16 May 2025
- 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning6 May 2025
- 2025HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast9 Mar 2025
- 2025Biased Federated Learning under Wireless Heterogeneity8 Mar 2025
- 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization1 Mar 2025
- 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data16 Jan 2025
- 2025Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks3 Jan 2025
- 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimization10 Dec 2024
- 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning10 Oct 2024
- 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning25 Jun 2024
- 2024FedAST: Federated Asynchronous Simultaneous Training1 Jun 2024
- 2024Federated Learning with Multi-resolution Model Broadcast30 May 2024
- 2024Federated Learning With Energy Harvesting Devices: An MDP Framework17 May 2024
- 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks23 Apr 2024
- 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates27 Mar 2024
- 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks19 Mar 2024
- 2024Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks5 Mar 2024
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligence26 Feb 2024
- 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices24 Feb 2024
- 2024Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching19 Feb 2024
- 2024DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service15 Feb 2024
- 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things4 Feb 2024
- 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach1 Feb 2024
- 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks18 Jan 2024
- 2024Joint Probability Selection and Power Allocation for Federated Learning15 Jan 2024
- 2023Efficient Asynchronous Federated Learning with Sparsification and Quantization23 Dec 2023
- 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selection28 Nov 2023
- 2023Accelerating Split Federated Learning over Wireless Communication Networks24 Oct 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
- 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks17 Sep 2023
- 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning17 Aug 2023
- 2023Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning18 Jul 2023
- 2023Data-Heterogeneous Hierarchical Federated Learning with Mobility19 Jun 2023
- 2023Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization22 May 2023
- 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference10 May 2023
- 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks18 Apr 2023
50 newest of 90 All 90 (JSON)
- 2021Federated Learning for Smart Healthcare: A Surveycited by 182
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
- 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
- 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
12 most cited of 90 All 90 (JSON)
- 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning14 Jul 2026
- 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks29 May 2026
- 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning25 May 2026
- 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation27 Apr 2026
- 2025TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learning6 Nov 2025
- 2025CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks24 Sep 2025
- 2025Hierarchical Federated Learning for Social Network with Mobility18 Sep 2025
- 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning9 Sep 2025
- 2025A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks5 Sep 2025
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity21 Jul 2025
- 2025Efficient Federated Learning with Timely Update Dissemination8 Jul 2025
- 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems10 Jun 2025
12 newest of 90 All 90 (JSON)