mlsanityData to 7 Oct 2026

Asynchronous Wireless Federated Learning with Probabilistic Client Selection

arXiv 2311.16741 ↗First posted 28 Nov 2023cs.LG cs.DC cs.NIcited by 4

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Abstract (arXiv)

Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server. To tackle the stragglers issue in asynchronous FL, we consider that each client keeps local updates and probabilistically transmits the local model to the server at arbitrary times. We first derive the (approximate) expression for the convergence rate based on the probabilistic client selection. Then, an optimization problem is formulated to trade off the convergence rate of asynchronous FL and mobile energy consumption by joint probabilistic client selection and bandwidth allocation. We develop an iterative algorithm to solve the non-convex problem globally optimally. Experiments demonstrate the superiority of the proposed approach compared with the traditional schemes.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 55 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

30 of 55 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020Adaptive Federated Optimizationcited by 1,036
  3. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  6. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  7. 2019Asynchronous Federated Optimizationcited by 264
  8. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  9. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  10. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
18 more
  1. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  2. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  3. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  4. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  5. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  6. 2021On Large-Cohort Training for Federated Learningcited by 73
  7. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  8. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  9. 2020Fast-Convergent Federated Learningcited by 61
  10. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  11. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 38
  12. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  13. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  14. 2019Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learningcited by 20
  15. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
  16. 2022Client Selection for Federated Bayesian Learningcited by 4
  17. 2020Data-Importance Aware Radio Resource Allocation: Wireless Communication Helps Machine Learningcited by 3
  18. 2021Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approachcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  2. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  3. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  4. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0