Federated Learning for Ultra-Reliable Low-Latency V2V Communications

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

243 citations recorded by the source corpus.

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reductions in extreme events of the proposed method is about two orders of magnitude.

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

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Citations in the corpus

On the Outsized Importance of Learning Rates in Local Update Methods (2020, background): “Federated learning has enabled privacy-aware learning in a variety of applications (Hard et al., 2018; Chen et al., 2019; Brisimi et al., 2018; Samarakoon et al., 2018; Hard et al., 2020), and has seen a large volume of work on the intersection of federated learning with topics including…”
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning (2019, background): “…(Hard et al., 2018; Yang et al., 2018), emoji prediction (Ramaswamy et al., 2019), decoder models (Chen et al., 2019b), vocabulary estimation (Chen et al., 2019a), low latency vehicle-to-vehicle communication (Samarakoon et al., 2018), and predictive models in health (Brisimi et al., 2018).”
Towards Federated Learning at Scale: System Design (2019, methodology): “In addition, Federated Learning has already been proposed in the context of vehicle-to-vehicle communication (Samarakoon et al., 2018) and medical applications (Brisimi et al.”
Federated Learning: Challenges, Methods, and Future Directions (2019, background): “Federated learning methods can help to train models that efficiently adapt to changes in these systems while maintaining user privacy [84, 98].”
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (2018, methodology): “Our prior work in [1] proposes a distributed FL-based algorithm for VUEs based on a maximum likelihood estimation (MLE).”
High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies (2019, background): “In [88], the inherent energy-latency tradeoff was investigated in HRLL communications with retransmissions.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “Further, FL has the potential to facilitate resource management strategies for vehicle-to-vehicle (V2V) networks [134].”
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale (2018, background): “Our preliminary work in this direction can be found in [53].”
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019, unknown): “[17] employed federated learning to estimate the tail distribution of network-wide queues, thus minimizing network wide power while ensuring ultrareliable and low-latency vehicular communication.”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “For example, the authors of [40] and [41] capitalize on”
Federated Learning for Healthcare Informatics (2019, background): “keyboard prediction [31,62], smart retail [99], finance [97], vehicle-to-vehicle communication [80].”
Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles (2020, background): “In [38], a joint transmit power and resource allocation approach”
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (2019, background): “, 2019a), low latency vehicle-to-vehicle communication (Samarakoon et al., 2018), and predictive models in health (Brisimi et al.”
Cloud-based Federated Boosting for Mobile Crowdsensing (2020, background): “[19] proposed a federated learning based novel joint transmit power and resource allocation approach.”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, methodology): “For multiple BSs to predict the quality of service of users [73] in FSL, each BS uses supervised learning algorithm based on its stored information such as users’ requested data, gender, job, and device type and all BSs transmit the learning model results to a server via NOMA to get a unified federa”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “Similar issues happen in other environments such as vehicle-to-vehicle networks [147].”