mlsanityData to 7 Oct 2026

Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning

arXiv 2107.11415 ↗First posted 23 Jul 2021cs.LG cs.DC cs.ITcited by 7

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Abstract (arXiv)

Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes. In this paper, we propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems. For the proposed model, we investigate several device scheduling and update aggregation policies and compare their performances when the devices have heterogeneous computation capabilities and training data distributions. From the simulation results, we conclude that the scheduling and aggregation design for asynchronous FL can be rather different from the synchronous case. For example, a norm-based significance-aware scheduling policy might not be efficient in an asynchronous FL setting, and an appropriate "age-aware" weighting design for the model aggregation can greatly improve the learning performance of such systems.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 23 Jul 2021.

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13 of 17 references.

References · 13

  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. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  4. 2019Asynchronous Federated Optimizationcited by 264
  5. 2017Revisiting Distributed Synchronous SGDcited by 142
  6. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  7. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  8. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  9. 2015Staleness-aware Async-SGD for Distributed Deep Learningcited by 43
  10. 2019Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networkscited by 39
  11. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  12. 2019Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learningcited by 20
1 more
  1. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15

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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 2.

7 papers.

  1. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  2. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  3. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  4. 2023Asynchronous Online Federated Learning with Reduced Communication Requirementscited by 2
  5. 2023Communication Trade-offs in Federated Learning of Spiking Neural Networkscited by 1
  6. 2025The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligencecited by 1
  7. 2025FedAvgen: Metadata for Model Aggregation In Communication Systemscited by 0