Delay Minimization for Federated Learning Over Wireless Communication Networks
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Abstract (arXiv)
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy level, which affects the convergence rate of the FL algorithm. This joint learning and communication problem is formulated as a delay minimization problem, where it is proved that the objective function is a convex function of the learning accuracy. Then, a bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 3; 2025: 2.
12 papers.
- 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
- 2022Trustworthy Federated Learning via Blockchaincited by 15
- 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
- 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
- 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
- 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
- 2022Time Minimization in Hierarchical Federated Learningcited by 4
- 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
- 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1
- 2025Adaptive Deadline and Batch Layered Synchronized Federated Learningcited by 1
- 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
- 2025ProbSelect: Stochastic Client Selection for GPU-Accelerated Compute Devices in the 3D Continuumcited by 0
- 2025ProbSelect: Stochastic Client Selection for GPU-Accelerated Compute Devices in the 3D Continuum11 Nov 2025
- 2025Adaptive Deadline and Batch Layered Synchronized Federated Learning29 May 2025
- 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach16 Sep 2024
- 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing11 Jul 2024
- 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things4 Feb 2024
- 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks17 Sep 2023
- 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning19 Oct 2022
- 2022Time Minimization in Hierarchical Federated Learning7 Oct 2022
- 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learning29 Sep 2022
- 2022Trustworthy Federated Learning via Blockchain13 Aug 2022
- 2021Quantized Federated Learning under Transmission Delay and Outage Constraints17 Jun 2021
- 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI1 Apr 2021