mlsanityData to 7 Oct 2026

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

arXiv 2002.00802 ↗First posted 3 Feb 2020cs.NIcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 4; 2023: 2; 2024: 2; 2025: 1; 2026: 2.

18 papers.

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  1. 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
  2. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  3. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  4. 2022SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharingcited by 11
  5. 2021Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learningcited by 10
  6. 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
  7. 2021A Hybrid Architecture for Federated and Centralized Learningcited by 8
  8. 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
  9. 2021Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networkscited by 7
  10. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  11. 2021Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffscited by 3
  12. 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
6 more
  1. 2023Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantizationcited by 1
  2. 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimizationcited by 1
  3. 2023Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learningcited by 0
  4. 2024Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimationcited by 0
  5. 2026VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSIcited by 0
  6. 2026Client and Training Data Selection for Computationally Efficient Synchronized Federated Learningcited by 0