On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems
| Autor: | Arash Bozorgchenani, Qiang Ni, Daniele Tarchi, Swapnil sadashiv Shinde |
|---|---|
| Přispěvatelé: | Shinde, Swapnil Sadashiv, Bozorgchenani, Arash, Tarchi, Daniele, Ni, Qiang |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Automotive Engineering Aerospace Engineering Electrical and Electronic Engineering Servers Costs Delays Edge computing Task analysis Resource management Optimization Vehicular Edge Computing Computation Offloading Federated Learning Latency Energy consumption Genetic Algorithm |
| Popis: | With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). Computation offloading has emerged as a viable option allowing to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning} framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|