Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles

Autor:	Pervej, Md Ferdous, Jin, Richeng, Dai, Huaiyu
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model. Convergence analysis reveals that the VEFL training loss depends on the successful receptions of the CVs' trained models over the intermittent vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the full device participation case (FDPC), the edge server aggregates client model parameters based on a weighted combination according to the CVs' dataset sizes and sojourn periods, while it selects a subset of CVs in the partial device participation case (PDPC). We then devise joint VEFL and radio access technology (RAT) parameters optimization problems under delay, energy and cost constraints to maximize the probability of successful reception of the locally trained models. Considering that the optimization problem is NP-hard, we decompose it into a VEFL parameter optimization sub-problem, given the estimated worst-case sojourn period, delay and energy expense, and an online RAT parameter optimization sub-problem. Finally, extensive simulations are conducted to validate the effectiveness of the proposed solutions with a practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility model. Comment: Accepted for publication in the IEEE Journal on Selected Areas in Communications (JSAC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.15496 Zobrazit plný text záznamu View this record from Arxiv