# Delay Minimization for Federated Learning Over Wireless Communication Networks

arXiv: https://arxiv.org/abs/2007.03462 · HTML: https://mlsanity.com/paper/2007.03462/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Quantized Federated Learning under Transmission Delay and Outage Constraints](https://mlsanity.com/paper/2106.09397/) (2021)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [Joint Optimization of Energy Consumption and Completion Time in Federated Learning](https://mlsanity.com/paper/2209.14900/) (2022)
- [Time Minimization in Hierarchical Federated Learning](https://mlsanity.com/paper/2210.04689/) (2022)
- [Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning](https://mlsanity.com/paper/2210.10311/) (2022)
- [User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks](https://mlsanity.com/paper/2309.09253/) (2023)
- [Adaptive Deadline and Batch Layered Synchronized Federated Learning](https://mlsanity.com/paper/2505.23973/) (2025)
- [A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach](https://mlsanity.com/paper/2409.11442/) (2024)
- [ProbSelect: Stochastic Client Selection for GPU-Accelerated Compute Devices in the 3D Continuum](https://mlsanity.com/paper/2511.08147/) (2025)

## 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.