University of Oulu

S. Samarakoon, M. Bennis, W. Saad and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications," in IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146-1159, Feb. 2020. doi: 10.1109/TCOMM.2019.2956472

Distributed federated learning for ultra-reliable low-latency vehicular communications

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Author: Samarakoon, Sumudu1; Bennis, Mehdi1,2; Saad, Walid3;
Organizations: 1Centre for Wireless Communication, University of Oulu, Finland
2Department of Computer Science and Engineering, Kyung Hee University, Seoul, South Korea
3Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA
4Mathematical and Algorithmic Sciences Lab, Huawei France R&D, Paris, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-01-31


In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide power consumption of vehicular users (VUEs) is minimized subject to high reliability in terms of probabilistic queuing delays. Using extreme value theory, a new reliability measure is defined to characterize extreme events pertaining to vehicles’ queue lengths exceeding a predefined threshold. To learn these extreme events, assuming they are independently and identically distributed over VUEs, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queue lengths. Considering the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed method to estimate the tail distribution of queues with an accuracy that is close to a centralized solution with up to 79% reductions in the amount of exchanged data. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 68
Issue: 2
Pages: 1146 - 1159
DOI: 10.1109/TCOMM.2019.2956472
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the Kvantum institute strategic project SAFARI, CARMA, MISSION, NOOR, SMARTER, the Academy of Finland 6Genesis Flagship project under grant 318927, and the U.S. National Science Foundation under Grants CNS-1836802 and OAC-1541105.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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