mlsanityData to 7 Oct 2026

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks

arXiv 2212.07356 ↗First posted 14 Dec 2022cs.LG cs.DC cs.ITcited by 20

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling policies and aggregation designs on the convergence performance. Driven by the importance of reducing the bias and variance of the aggregated model updates, we propose a scheduling policy that jointly considers the channel quality and training data representation of user devices. The effectiveness of our channel-aware data-importance-based scheduling policy, compared with state-of-the-art methods proposed for synchronous FL, is validated through simulations. Moreover, we show that an ``age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

29 of 42 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  4. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  5. 2014Convex Optimization: Algorithms and Complexitycited by 569
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  7. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  8. 2019Asynchronous Federated Optimizationcited by 264
  9. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  10. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  11. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  12. 2017Revisiting Distributed Synchronous SGDcited by 142
17 more
  1. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  2. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  3. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  4. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  5. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  6. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  7. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  8. 2020SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimizationcited by 31
  9. 2018New Convergence Aspects of Stochastic Gradient Algorithmscited by 30
  10. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  11. 2019Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learningcited by 20
  12. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
  13. 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
  14. 2021Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learningcited by 10
  15. 2021Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Schedulingcited by 9
  16. 2022Data-Quality Based Scheduling for Federated Edge Learningcited by 6
  17. 2022Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selectioncited by 3

Built on by

Cited by year

Citing papers per year: 2023: 7; 2024: 8; 2025: 5.

20 papers.

Sort:

  1. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  2. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  3. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  4. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  5. 2023Asynchronous Online Federated Learning with Reduced Communication Requirementscited by 2
  6. 2023Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networkscited by 2
  7. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  8. 2024Temporal Predictive Coding for Gradient Compression in Distributed Learningcited by 2
  9. 2023Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?cited by 1
  10. 2023Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theorycited by 1
  11. 2024Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigationcited by 1
  12. 2024Optimal Batch Allocation for Wireless Federated Learningcited by 1
8 more
  1. 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networkscited by 1
  2. 2024A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learningcited by 1
  3. 2025The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligencecited by 1
  4. 2025Adaptive Deadline and Batch Layered Synchronized Federated Learningcited by 1
  5. 2023FedAgg: Adaptive Federated Learning with Aggregated Gradientscited by 0
  6. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learningcited by 0
  7. 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsificationcited by 0
  8. 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Thingscited by 0