# Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems

arXiv: https://arxiv.org/abs/2212.00491 · HTML: https://mlsanity.com/paper/2212.00491/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2022.

## Built on by (most cited first, 20 of 20)

- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm](https://mlsanity.com/paper/2404.00836/) (2024)
- [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)
- [Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks](https://mlsanity.com/paper/2402.04216/) (2024)
- [Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data](https://mlsanity.com/paper/2404.06776/) (2024)
- [Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach](https://mlsanity.com/paper/2406.09776/) (2024)
- [Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks](https://mlsanity.com/paper/2302.08447/) (2023)
- [Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels](https://mlsanity.com/paper/2310.16407/) (2023)
- [Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning](https://mlsanity.com/paper/2312.14638/) (2023)
- [OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning](https://mlsanity.com/paper/2403.04037/) (2024)
- [Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy](https://mlsanity.com/paper/2407.00955/) (2024)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture](https://mlsanity.com/paper/2503.18284/) (2025)
- [VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning](https://mlsanity.com/paper/2311.18741/) (2023)
- [Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning](https://mlsanity.com/paper/2312.12417/) (2023)
- [Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection](https://mlsanity.com/paper/2410.17792/) (2024)
- [Lightweight Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2507.09546/) (2025)
- [Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning](https://mlsanity.com/paper/2603.10763/) (2026)
- [Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling](https://mlsanity.com/paper/2609.08312/) (2026)

## Abstract (arXiv)

To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and privacy-preserving. To further improve the efficiency of wireless data aggregation and model learning, over-the-air computation (AirComp) is emerging as a promising solution by using the superposition characteristics of wireless channels. However, the fading and noise of wireless channels can cause aggregate distortions in AirComp enabled federated learning. In addition, the quality of collected data and energy consumption of edge devices may also impact the accuracy and efficiency of model aggregation as well as convergence. To solve these problems, this work proposes a dynamic device scheduling mechanism, which can select qualified edge devices to transmit their local models with a proper power control policy so as to participate the model training at the server in federated learning via AirComp. In this mechanism, the data importance is measured by the gradient of local model parameter, channel condition and energy consumption of the device jointly. In particular, to fully use distributed datasets and accelerate the convergence rate of federated learning, the local updates of unselected devices are also retained and accumulated for future potential transmission, instead of being discarded directly. Furthermore, the Lyapunov drift-plus-penalty optimization problem is formulated for searching the optimal device selection strategy. Simulation results validate that the proposed scheduling mechanism can achieve higher test accuracy and faster convergence rate, and is robust against different channel conditions.