Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

By Sumudu Samarakoon, Medhi Bennis, Walid Saad, Merouane Debbah. 2018

420 citations recorded by the source corpus.

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 (EVT), 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Federated & Distributed Learning

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Citations in the corpus

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization (2019, background): “Examples of such applications are learning from wearable devices [Huang et al., 2018], learning sentiment [Smith et al., 2017], and location-based services [Samarakoon et al., 2018].”
Wireless Network Intelligence at the Edge (2018, methodology): “EVT-based FL enables URLLC in vehicular communication networks as discussed in our preliminary study [209].”
Federated Class-Incremental Learning (2022, background): “, autonomous driving [39], wearable devices [33], medical diagnosis [10, 52] and mobile phones [36].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “Due to the resource constraints of mobile devices, extensive-computational tasks including mining,”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “To minimize the vehicles’ queuing latency, a FL approach was developed in [300] to learn the tail distribution of the queue lengths.”
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks (2019, background): “The authors in [16] studied the problem of joint power and resource allocation for ultra-reliable low latency communication in vehicular networks.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “Naturally, these devices can be extended more widely in edge computing [200], [201].”
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing (2020, methodology): “The federated learning [14], which is a new-merging technology, can achieve efﬁciency communication by exchanging updates between local models and global models [2].”
Federated Learning in Vehicular Networks (2020, background): “As a new emerging field still in its infancy, there are a very limited number of works considering FL in vehicular applications that satisfy these three conditions [6, 7].”