# Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

arXiv: https://arxiv.org/abs/2002.00802 · HTML: https://mlsanity.com/paper/2002.00802/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization](https://mlsanity.com/paper/2201.07912/) (2022)
- [Cost-Effective Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/2109.05411/) (2021)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing](https://mlsanity.com/paper/2202.05800/) (2022)
- [Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning](https://mlsanity.com/paper/2106.06796/) (2021)
- [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021)
- [A Hybrid Architecture for Federated and Centralized Learning](https://mlsanity.com/paper/2105.03288/) (2021)
- [Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning](https://mlsanity.com/paper/2106.12561/) (2021)
- [Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2108.08768/) (2021)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs](https://mlsanity.com/paper/2110.01686/) (2021)
- [Matching Pursuit Based Scheduling for Over-the-Air Federated Learning](https://mlsanity.com/paper/2206.06679/) (2022)
- [Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization](https://mlsanity.com/paper/2305.10852/) (2023)
- [Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization](https://mlsanity.com/paper/2503.00569/) (2025)
- [Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning](https://mlsanity.com/paper/2301.11903/) (2023)
- [Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation](https://mlsanity.com/paper/2404.03088/) (2024)
- [VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI](https://mlsanity.com/paper/2602.03711/) (2026)
- [Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning](https://mlsanity.com/paper/2609.39250/) (2026)

## Abstract (arXiv)

In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI). First, the problem is cast as a stochastic optimization problem over a predefined training duration and solved using the Lyapunov optimization framework. In order to learn and track the wireless channel, a Gaussian process regression (GPR)-based channel prediction method is leveraged and incorporated into the scheduling decision. The proposed scheduling policies are evaluated via numerical simulations, under both perfect and imperfect CSI. Results show that the proposed method reduces the loss of accuracy up to 25.8% compared to state-of-the-art client scheduling and RB allocation methods.