# Scheduling Policies for Federated Learning in Wireless Networks

arXiv: https://arxiv.org/abs/1908.06287 · HTML: https://mlsanity.com/paper/1908.06287/

## Key facts

- Cited by 131 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 30 of 131)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization](https://mlsanity.com/paper/2201.07912/) (2022)
- [Low-Latency Federated Learning over Wireless Channels with Differential Privacy](https://mlsanity.com/paper/2106.13039/) (2021)
- [Cost-Effective Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/2109.05411/) (2021)
- [Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks](https://mlsanity.com/paper/2212.07356/) (2022)
- [SplitGP: Achieving Both Generalization and Personalization in Federated Learning](https://mlsanity.com/paper/2212.08343/) (2022)
- [Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking](https://mlsanity.com/paper/2205.13054/) (2022)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022)
- [Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning](https://mlsanity.com/paper/2103.01422/) (2021)
- [Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT](https://mlsanity.com/paper/2202.03575/) (2022)
- [Wireless Quantized Federated Learning: A Joint Computation and Communication Design](https://mlsanity.com/paper/2203.05878/) (2022)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis](https://mlsanity.com/paper/2104.10095/) (2021)
- [Scalable Hierarchical Over-the-Air Federated Learning](https://mlsanity.com/paper/2211.16162/) (2022)
- [Faithful Edge Federated Learning: Scalability and Privacy](https://mlsanity.com/paper/2106.15905/) (2021)
- [Federated Learning over Wireless IoT Networks with Optimized Communication and Resources](https://mlsanity.com/paper/2110.11775/) (2021)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer](https://mlsanity.com/paper/2102.12357/) (2021)

## Abstract (arXiv)

Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.