Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning
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Abstract (arXiv)
Federated learning over IEEE~802.11 shares the wireless channel among clients that send model updates. We use ns-3 to measure the frame-delivery ratio and saturation throughput for different client densities and offered loads. A separate FedAvg trainer uses the frame-delivery ratio as a first-order proxy for the update-admission probability and uses an equation to estimate communication time. The method does not simulate the delivery of a complete model update or measure end-to-end training time. Across 720 evaluated runs with two datasets, two data partitions, six client densities, six offered loads, and five seeds, all runs reached their predefined target accuracy within the round budget. Rounds-to-target changed little with offered load, while communication time-to-target increased by about two orders of magnitude across the client-density range. A Bianchi-anchored estimator produced a mean absolute percentage error from 2.3% to 10.2% on held-out configurations. This error is measured against communication time constructed from the same round-duration equation, not against independently measured completion time. We also compare uniform participation with persistent heterogeneous participation. The study does not detect a statistically distinguishable excluded-class accuracy gap over five seeds, but the confidence intervals are wide. The results apply only to the evaluated configurations and do not provide a general convergence or fairness guarantee.
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References · 12
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
- 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
- 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
- 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
- 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
- 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
- 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
- 2022Rate-Convergence Tradeoff of Federated Learning over Wireless Channelcited by 2
- 2024Federated Deep Reinforcement Learning-Based Intelligent Channel Access in Dense Wi-Fi Deploymentscited by 1
- 2025Falafels: A tool for Estimating Federated Learning Energy Consumption via Discrete Simulationcited by 1
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