Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning
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The performance of federated learning (FL) over wireless networks depend on the reliability of the client-server connectivity and clients' local computation capabilities. In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources. First, we analytically derive the gap between the training losses of FL with clients scheduling and a centralized training method for a given training duration. Then, we formulate the gap of the training loss minimization over client scheduling and RB allocation as a stochastic optimization problem and solve it using Lyapunov optimization. A Gaussian process regression-based channel prediction method is leveraged to learn and track the wireless channel, in which, the clients' CSI predictions and computing power are incorporated into the scheduling decision. Using an extensive set of simulations, we validate the robustness of the proposed method under both perfect and imperfect CSI over an array of diverse data distributions. Results show that the proposed method reduces the gap of the training accuracy loss by up to 40.7% compared to state-of-the-art client scheduling and RB allocation methods.
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In large-scale edge AI systems, estimated CSI will be inevitably imperfect or partially available.
“In large-scale edge AI systems, the estimated CSI will be inevitably imperfect or partially available [177], [271].”
From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [271]
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Research works printed but not linked: 5
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- Authors
- Wadu, M. M., Samarakoon, S., & Bennis, M.
- Affiliations
- University of Oulu (FI)
- Venue
- IEEE Transactions on Communications
- Published
- 2021-09
- Volume
- 69
- Issue
- 9
- Pages
- 5962-5974
- ISSN
- 0090-6778 (ISSN-L)
- 1558-0857
- OpenAlex
- W3166884572
- Cited by
- 2
- References
- 46
- Citations by year
- 2026: 2
- FWCI
- 0.00
- 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
- Academy of Finland 6G Flagship (318927)
- project SMARTER, projects EU-ICT IntellIoT and EUCHISTERA LearningEdge, Infotech-NOOR
- 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, user scheduling, resource block allocation, channel uncertainty, imperfect channel state information, wireless networks, Lyapunov optimization, stochastic optimization, Gaussian process regression, channel prediction
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