# Device Scheduling with Fast Convergence for Wireless Federated Learning

arXiv: https://arxiv.org/abs/1911.00856 · HTML: https://mlsanity.com/paper/1911.00856/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2019.

## Built on by (most cited first, 30 of 46)

- [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021)
- [Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design](https://mlsanity.com/paper/2103.14272/) (2021)
- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [Quantized Federated Learning under Transmission Delay and Outage Constraints](https://mlsanity.com/paper/2106.09397/) (2021)
- [Efficient Device Scheduling with Multi-Job Federated Learning](https://mlsanity.com/paper/2112.05928/) (2021)
- [Cost-Effective Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/2109.05411/) (2021)
- [Context-Aware Online Client Selection for Hierarchical Federated Learning](https://mlsanity.com/paper/2112.00925/) (2021)
- [FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity](https://mlsanity.com/paper/2206.10546/) (2022)
- [Multi-Job Intelligent Scheduling with Cross-Device Federated Learning](https://mlsanity.com/paper/2211.13430/) (2022)
- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments](https://mlsanity.com/paper/2111.10635/) (2021)
- [Task-Oriented Over-the-Air Computation for Multi-Device Edge AI](https://mlsanity.com/paper/2211.01255/) (2022)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset](https://mlsanity.com/paper/2310.14906/) (2023)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)
- [CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework](https://mlsanity.com/paper/2104.08184/) (2021)
- [Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2404.13804/) (2024)
- [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://mlsanity.com/paper/2101.02198/) (2021)
- [Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation](https://mlsanity.com/paper/2304.07981/) (2023)
- [Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2108.08768/) (2021)
- [Confederated Learning: Federated Learning with Decentralized Edge Servers](https://mlsanity.com/paper/2205.14905/) (2022)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying](https://mlsanity.com/paper/2202.11850/) (2022)
- [Collaborative Learning over Wireless Networks: An Introductory Overview](https://mlsanity.com/paper/2112.05559/) (2021)
- [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)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks](https://mlsanity.com/paper/2311.04350/) (2023)

## Abstract (arXiv)

Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged. In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels. Due to the limited spectrum resources, only a portion of the devices can be scheduled in each round. While most of the existing work on scheduling focuses on the convergence of FL w.r.t. rounds, the convergence performance under a total training time budget is not yet explored. In this paper, a joint bandwidth allocation and scheduling problem is formulated to capture the long-term convergence performance of FL, and is solved by being decoupled into two sub-problems. For the bandwidth allocation sub-problem, the derived optimal solution suggests to allocate more bandwidth to the devices with worse channel conditions or weaker computation capabilities. For the device scheduling sub-problem, by revealing the trade-off between the number of rounds required to attain a certain model accuracy and the latency per round, a greedy policy is inspired, that continuously selects the device that consumes the least time in model updating until achieving a good trade-off between the learning efficiency and latency per round. The experiments show that the proposed policy outperforms other state-of-the-art scheduling policies, with the best achievable model accuracy under training time budgets.