University of Oulu

M. M. Wadu, S. Samarakoon and M. Bennis, "Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning," in IEEE Transactions on Communications, vol. 69, no. 9, pp. 5962-5974, Sept. 2021, doi: 10.1109/TCOMM.2021.3088528

Joint client scheduling and resource allocation under channel uncertainty in federated learning

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Author: Wadu, Madhusanka Manimel1; Samarakoon, Sumudu1; Bennis, Mehdi1
Organizations: 1Centre for Wireless Communications (CWC), University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021101150547
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-11
Description:

Abstract

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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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 69
Issue: 9
Pages: 5962 - 5974
DOI: 10.1109/TCOMM.2021.3088528
OADOI: https://oadoi.org/10.1109/TCOMM.2021.3088528
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is supported by Academy of Finland 6G Flagship (grant no. 318927) and project SMARTER, projects EU-ICT IntellIoT and EUCHISTERA LearningEdge, Infotech-NOOR.
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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