Edge computing-based joint client selection and networking scheme for federated learning in vehicular IoT

Bao, Wugedele; Wu, Celimuge; Guleng, Siri; Zhang, Jie-Fang; Yau, Kok‐Lim Alvin; Ji, Yusheng

doi:10.23919/jcc.2021.06.004

Cited by 47 publications

(16 citation statements)

References 32 publications

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“…In [35], the authors have studied FL in a VEC scenario and have proposed an approach for selecting the best quality models during the training phase for tackling the diverse data quality and corresponding information asymmetry issue of the FL process. An edge computing-based joint client selection and networking scheme for vehicular IoT is presented in [36]. The importance of the trade-off between the accuracy of the global model and the communication overhead of FL in vehicular environments is also highlighted.…”

Section: Related Workmentioning

confidence: 99%

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

Shinde

Bozorgchenani

Tarchi

et al. 2022

IEEE Trans. Veh. Technol.

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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.

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Section: Related Workmentioning

confidence: 99%

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

Shinde

Bozorgchenani

Tarchi

et al. 2022

IEEE Trans. Veh. Technol.

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“…However, the framework lacks an analysis of the loss and accuracy incurred in training the network model data. Bao et al (2021) used distributed approaches to designate some vehicles as edge vehicles and used the edge vehicles as federated learning clients for local model training, resulting in an efficient deep learning network architecture. Also, Zhao et al (2020) advanced federated learning and local differential privacy (LDP), as well as four LDP mechanisms to scramble the gradients generated by the vehicles, to provide high accuracy with a small privacy budget.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Also, Zhao et al (2020) advanced federated learning and local differential privacy (LDP), as well as four LDP mechanisms to scramble the gradients generated by the vehicles, to provide high accuracy with a small privacy budget. The literature ( Pokhrel & Choi, 2020b ; Bao et al, 2021 ; Zhao et al, 2020 ) amply illustrates that the combination of vehicular networking and federated learning can be a solution to the problem of isolated data islands in the IoV. Although the models in the literature can improve the training accuracy of the network models, the global models are susceptible to noise data, leading to biases that make the global models inefficient to learn and slow to converge.…”

Section: Literature Reviewmentioning

confidence: 99%

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei

Wang

Caiyu

et al. 2022

PeerJ Computer Science

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The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.

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“…An algorithm of weighted zero-forcing precoding is used by each of UAV to mitigate the interference to the FL server. Bao et al 8 proposed an edge computing-based joint client selection and networking scheme for vehicular IoT, where some of vehicles are assigned to act as both edge nodes (aka cluster centers) and FL clients via a distributed approach. The selected clients play a role of forwarders between common vehicles and FL server.…”

Section: Related Workmentioning

confidence: 99%

“…5,6 Recent 2 years have begun to witness an increasing interest in studying how to employ FL on mobile ad hoc networks (MANET) or multi-agent systems (MAS), such as unmanned aerial vehicles (UAV), 7 and vehicular Internet of Things (IoT). 8 Nevertheless, these mobile devices acting as FL clients are designed to directly communicate with an FL server (or a cluster center that plays a role in relaying), rather than an ad hoc operation mode. This situation is also present when FL is applied to wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Distributed consensus problem with caching on federated learning framework

Yan

Qin

et al. 2022

International Journal of Distributed Sensor Networks

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Federated learning framework facilitates more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of federated learning participants are often inaccessible, such as in a power shortage or dormant state. That will force us to explore the possibility that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. On the contrary, since caching mechanism is indispensable to any federated learning mobile node, it is necessary to investigate the connection between it and consensus computing. In this article, we first propose a novel federated learning paradigm, which supports an ad hoc operation mode for federated learning participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from federated learning framework, with a quantized caching scheme designed to mask the uncertainties from both asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a quantized caching strategy to optimize the convergence speed is provided. Our major contribution is to give the basic theories of distributed consensus problem for federated learning framework, and the theoretical results are validated by numerical simulations.

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Edge computing-based joint client selection and networking scheme for federated learning in vehicular IoT

Cited by 47 publications

References 32 publications

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

On the Design of Federated Learning in Latency and Energy Constrained Computation Offloading Operations in Vehicular Edge Computing Systems

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Distributed consensus problem with caching on federated learning framework

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