mlsanityData to 14 Oct 2026

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

arXiv 2106.06796 ↗First posted 12 Jun 2021cs.LGcited by 10

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Abstract (arXiv)

The performance of federated learning (FL) over wireless networks depend on the reliability of the client-server connectivity and clients' local computation capabilities. In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources. First, we analytically derive the gap between the training losses of FL with clients scheduling and a centralized training method for a given training duration. Then, we formulate the gap of the training loss minimization over client scheduling and RB allocation as a stochastic optimization problem and solve it using Lyapunov optimization. A Gaussian process regression-based channel prediction method is leveraged to learn and track the wireless channel, in which, the clients' CSI predictions and computing power are incorporated into the scheduling decision. Using an extensive set of simulations, we validate the robustness of the proposed method under both perfect and imperfect CSI over an array of diverse data distributions. Results show that the proposed method reduces the gap of the training accuracy loss by up to 40.7% compared to state-of-theart client scheduling and RB allocation methods.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 12 Jun 2021.

Builds on

21 of 47 references.

References · 21

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2021Convex optimizationcited by 2,515
  3. 2018Federated Learning with Non-IID Datacited by 1,108
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2018Federated Learning via Over-the-Air Computationcited by 219
  9. 2019Federated Learning over Wireless Fading Channelscited by 183
  10. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  11. 2018Blockchained On-Device Federated Learningcited by 106
  12. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 96
9 more
  1. 2018Wireless Network Intelligence at the Edgecited by 92
  2. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  3. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  4. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  5. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  6. 2020Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocationcited by 18
  7. 2013Gaussian Processes for Nonlinear Signal Processingcited by 9
  8. 2013Adaptive Video Streaming for Wireless Networks with Multiple Users and Helperscited by 1
  9. 2019Smart Scheduling and Feedback Allocation over Non-stationary Wireless Channelscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

10 papers.

  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  2. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  3. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  4. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  5. 2021Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networkscited by 3
  6. 2022Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learningcited by 1
  7. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
  8. 2021Multi-task Federated Edge Learning (MtFEEL) in Wireless Networkscited by 0
  9. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  10. 2026Client and Training Data Selection for Computationally Efficient Synchronized Federated Learningcited by 0