# Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

arXiv: https://arxiv.org/abs/2203.14753 · HTML: https://mlsanity.com/paper/2203.14753/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2022.

## Built on by (most cited first, 10 of 10)

- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning](https://mlsanity.com/paper/2501.01828/) (2025)
- [Towards Scalable Wireless Federated Learning: Challenges and Solutions](https://mlsanity.com/paper/2310.05076/) (2023)
- [Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients](https://mlsanity.com/paper/2310.07171/) (2023)
- [Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning](https://mlsanity.com/paper/2310.17890/) (2023)
- [Age-Aware Edge-Blind Federated Learning via Over-the-Air Aggregation](https://mlsanity.com/paper/2602.02469/) (2026)
- [Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning](https://mlsanity.com/paper/2308.09072/) (2023)
- [Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning](https://mlsanity.com/paper/2603.10763/) (2026)

## Abstract (arXiv)

In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perform multiple local iterations in each communication round and then concurrently transmit their updated gradients to an edge server over the same radio channel for global model aggregation using over-the-air computation (AirComp). We derive the upper bound of the time-average norm of the gradients to characterize the convergence of AirComp-assisted FL, which reveals the impact of the model aggregation errors accumulated over all communication rounds on convergence. Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirComp-assisted FL. As the alternating optimization algorithm suffers from high computation complexity, we further develop a knowledge-guided learning algorithm that exploits the structure of the analytic expression of the optimal transmit power to achieve computation-efficient transceiver design. Simulation results demonstrate that the proposed knowledge-guided learning algorithm achieves a comparable performance as the alternating optimization algorithm, but with a much lower computation complexity. Moreover, both proposed algorithms outperform the baseline methods in terms of convergence speed and test accuracy.