Scheduling Policies for Federated Learning in Wireless Networks
Open accessAbstract
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.
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The convergence rate of scheduling policies including random scheduling, round-robin, and proportional fair is largely dependent on the signal quality threshold.
“Three scheduling schemes are examined in [43], including random scheduling, round-robin, and proportional fair, showing that the convergence rate of these scheduling policies is largely dependent on the signal quality threshold.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [43]
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- Authors
- Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V.
- Affiliations
- Singapore University of Technology and Design (SG)
- National University of Singapore (SG)
- Princeton University (US)
- Venue
- IEEE Transactions on Communications
- Published
- 2020-01
- Volume
- 68
- Issue
- 1
- Pages
- 317-333
- ISSN
- 0090-6778 (ISSN-L)
- 1558-0857
- OpenAlex
- W2968239693
- Cited by
- 640
- References
- 41
- Citations by year
- 2026: 34 · 2025: 53 · 2024: 99 · 2023: 124 · 2022: 127 · 2021: 130 · 2020: 73
- FWCI
- 37.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- SUTD-ZJU Research Collaboration (SUTD- ZJU/RES/01/2016, SUTD-ZJU/RES/05/2016)
- SUTD Growth Plan Grant for AI
- National Science Foundation (CCF-093970, CCF-1513915)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, scheduling policies, wireless edge networks, convergence rate, inter-cell interference, proportional fair scheduling, round robin scheduling, probabilistic scheduling, SINR threshold, parameter compression, quantization, user equipment
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