Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks

Saputra, Yuris Mulya; Nguyen, Diep N.; Dinh, Hoang Thai; Vu, Thang X.; Dutkiewicz, Eryk; Chatzinotas, Symeon

doi:10.1109/tmc.2020.3045987

Cited by 47 publications

(13 citation statements)

References 41 publications

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“…Profit maximization for both buyer and sellers, QoI improvement for sellers, IR and IC [136] Electric vehicle networks SGP CSs Power Same as [135] Profit maximization for buyers, IC,and IR [137] [139]…”

Section: Vehicles' Local Training Resultsmentioning

confidence: 99%

“…The economic approach in [135] is also applied into the electric vehicle network as proposed in [136]. In this network, multiple charging stations (CSs), i.e., principals (employers), firstly implement the energy demand prediction method leveraging FL, and then reserve energy from the smart grid provider (SGP) in advance based on the prediction results to maximize their own profits.…”

Section: A Incentive Mechanisms Based On Contract Theorymentioning

confidence: 99%

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Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Tu¹,

Luong²,

Niyato³

et al. 2021

Preprint

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Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for incentivizing data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.

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Section: Vehicles' Local Training Resultsmentioning

confidence: 99%

Section: A Incentive Mechanisms Based On Contract Theorymentioning

confidence: 99%

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Tu¹,

Luong²,

Niyato³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As part of IoV networks, electric vehicle (EV) networks are becoming more popular as the number of EVs increases [106]; such networks are expected to take over from traditional vehicles in the coming years. The work in [107] studied energy efficiency and profit maximisation at charging stations (CSs). They proposed an FL-based economically efficient framework, with the intention to investigate the historical energy transactions, which are essential for increasing the CSs profit.…”

Section: ) Cellular Networkmentioning

confidence: 99%

“…Using their limited energy storage capabilities, the CSs store energy received from smart grid providers (SGPs). However, SGPs do not disclose its private information to the CSs that can be exploited to maximise the CS profit, so the authors in [107] developed a multi-principal one-agent contract to optimise each CS profit by asking the SGP to reserve energy in advance.…”

Section: ) Cellular Networkmentioning

confidence: 99%

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan¹,

Mohjazi²,

Bariah³

et al. 2021

Preprint

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The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

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“…A FL-based traffic prediction algorithm is presented in [10], while in [11] a federated transfer reinforcement learning approach is developed for realtime knowledge extraction. Other studies focus on model selection for aggregation [12] or contract-based FL to maximize energy efficiency in electric vehicle networks [13]. Finally, [14] presents an initial feasibility study on FL for vehicular networks.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Federated Learning for Road User Classification in Enhanced V2X Networks

Barbieri

Savazzi

Nicoli

2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

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Federated Learning (FL) techniques are emerging in the automotive context to support connected automated driving services. Yet, when applied to vehicular use cases, conventional centralized FL policies show some drawbacks in terms of latency and scalability. This paper focuses on decentralized FL solutions, which attempt to overcome such limitations, by introducing a distributed computing architecture: vehicles exchange the parameters of a shared Machine Learning (ML) model via V2V links, without the need of a central orchestrator. Sharing all ML parameters, however, might not be feasible when minimal V2X bandwidth usage is required or the model is highly complex (e.g., extremely deep networks) as in advanced scenarios for high levels of automation. We thus propose a modular decentralized FL solution and we discuss its application to road user classification in a cooperative vehicular sensing use case. The proposed FL solution performs the point cloud processing of Lidar sensor inputs using a PointNet compliant architecture. It enables the exchange of a subset of the model parameters, namely selected ML model layers, optimized for communication efficiency, convergence and accuracy. We use real sensor data extracted from a publicly available dataset to validate the method, focusing on non-uniform scenarios where sensor data are highly unbalanced across the connected vehicles. For all cases, FL is shown to outperform the ego-sensing approach with minimal bandwidth usage.

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Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks

Cited by 47 publications

References 41 publications

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Decentralized Federated Learning for Road User Classification in Enhanced V2X Networks

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