mlsanityData to 14 Oct 2026

Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning

arXiv 2103.01422 ↗First posted 2 Mar 2021cs.IT cs.LG math.ITcited by 15

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Abstract (arXiv)

In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

24 of 38 references.

References · 24

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12 more
  1. 2016Why is Posterior Sampling Better than Optimism for Reinforcement Learning?cited by 117
  2. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  3. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 100
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  6. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
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Citing papers per year: 2021: 1; 2022: 4; 2023: 6; 2024: 3; 2025: 1.

15 papers.

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  1. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  2. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  3. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  4. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  5. 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computingcited by 9
  6. 2021Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learningcited by 7
  7. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
  8. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  9. 2023Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learningcited by 3
  10. 2024GAS: Generative Activation-Aided Asynchronous Split Federated Learningcited by 3
  11. 2023FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropoutcited by 2
  12. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
3 more
  1. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  2. 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
  3. 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsificationcited by 0