# Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness

arXiv: https://arxiv.org/abs/2004.00490 · HTML: https://mlsanity.com/paper/2004.00490/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 30 of 62)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning](https://mlsanity.com/paper/2201.09531/) (2022)
- [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021)
- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data](https://mlsanity.com/paper/2105.07066/) (2021)
- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization](https://mlsanity.com/paper/2201.07912/) (2022)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients](https://mlsanity.com/paper/2401.02433/) (2023)
- [Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking](https://mlsanity.com/paper/2205.13054/) (2022)
- [DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing](https://mlsanity.com/paper/2210.02680/) (2022)
- [Accelerating Split Federated Learning over Wireless Communication Networks](https://mlsanity.com/paper/2310.15584/) (2023)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2103.02270/) (2021)
- [Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning](https://mlsanity.com/paper/2106.06796/) (2021)
- [Over-the-Air Federated Edge Learning with Hierarchical Clustering](https://mlsanity.com/paper/2207.09232/) (2022)
- [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning](https://mlsanity.com/paper/2305.16854/) (2023)
- [FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion](https://mlsanity.com/paper/2311.09540/) (2023)
- [Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles](https://mlsanity.com/paper/2101.00191/) (2021)
- [Energy-Harvesting Distributed Machine Learning](https://mlsanity.com/paper/2102.05639/) (2021)
- [Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design](https://mlsanity.com/paper/2211.04132/) (2022)

## Abstract (arXiv)

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.