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

Tu, Xuezhen; Zhu, Kun; Luong, Nguyen Cong; Niyato, Dusit; Zhang, Yang; Li, Juan

doi:10.1109/tccn.2022.3177522

Cited by 83 publications

(19 citation statements)

References 134 publications

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“…1) Theories Behind Mechanism Design: We classify underlying theories for MD broadly into two categories, namely: 1) game theory and 2) auctions, based on [33].…”

Section: Incentive Mechanismmentioning

confidence: 99%

“…To the best of our knowledge, this is the first analysis of holistic frameworks for fully decentralized FL with rewards for the participating clients. Yet, we have identified several survey papers in the context of either MD and FL [8], [33], [42] or BC and FL [8], [43], [44]. Table I shows the comparison of the related survey papers and our own.…”

Section: Related Surveysmentioning

confidence: 99%

“…The other related survey papers focus on IM for FL [8], [33], [42], [44]. Zhan et al [42] surveyed the IM design dedicated to FL.…”

Section: Related Surveysmentioning

confidence: 99%

“…Besides [8] and [42], Ali et al [44] also summarized involved actors (e.g., number of publishers and workers), evaluation data sets as well as advantages and disadvantages of the mechanisms and security considerations. Tu et al [33] provided a comprehensive review of economic and game theoretic approaches to incentivize data owners to participate in FL. In particular, they cluster applications of Stackelberg games, noncooperative games, sealed-bid auction models, reverse action models as well as contract and matching theory for incentive MD in FL.…”

Section: Related Surveysmentioning

confidence: 99%

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Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt

Heyer

Toyoda

et al. 2023

IEEE Internet Things J.

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The advent of federated learning (FL) has sparked a new paradigm of parallel and confidential decentralized machine learning (ML) with the potential of utilizing the computational power of a vast number of Internet of Things (IoT), mobile, and edge devices without data leaving the respective device, thus ensuring privacy by design. Yet, simple FL frameworks (FLFs) naively assume an honest central server and altruistic client participation. In order to scale this new paradigm beyond small groups of already entrusted entities toward mass adoption, FLFs must be: 1) truly decentralized and 2) incentivized to participants. This systematic literature review is the first to analyze FLFs that holistically apply both, the blockchain technology to decentralize the process and reward mechanisms to incentivize participation. 422 publications were retrieved by querying 12 major scientific databases. After a systematic filtering process, 40 articles remained for an in-depth examination following our five research questions. To ensure the correctness of our findings, we verified the examination results with the respective authors. Although having the potential to direct the future of distributed and secure artificial intelligence, none of the analyzed FLFs is production ready. The approaches vary heavily in terms of use cases, system design, solved issues, and thoroughness. We provide a systematic approach to classify and quantify differences Manuscript

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“…1) Theories Behind Mechanism Design: We classify underlying theories for MD broadly into two categories, namely: 1) game theory and 2) auctions, based on [33].…”

Section: Incentive Mechanismmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

“…The other related survey papers focus on IM for FL [8], [33], [42], [44]. Zhan et al [42] surveyed the IM design dedicated to FL.…”

Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

See 2 more Smart Citations

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt

Heyer

Toyoda

et al. 2023

IEEE Internet Things J.

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“…Incentive mechanisms in FL refer to the improvement in the quality of data resulting in better accuracy with fewer iterations, thus leading to better overall efficiency and energy-saving measures. The authors of [3,117,118] described such an incentive mechanism that would reward efforts towards achieving a higher quality of data. The authors of [118] provided an incentive mechanism related to the design of federated learning and chose to use game theory and action theory for designing the incentive mechanism.…”

Section: Incentivesmentioning

confidence: 99%

Federated Learning Design and FunctionalModels: Survey

John

Utomo

Rouniyar

et al. 2022

Preprint

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Federated learning is a multiple device collaboration setup designed tosolve machine learning problems under framework for aggregation andknowledge transfer in distributed local data. This distributed modelensures the privacy of data at each local node. Owing to its relevance,there has been extensive research activities and outcomes in federatedlearning with expanded applicability to different areas by the researchcommunity. As such, there is a vast research archive made available by thecommunity with research work and articles related to the various aspectsof federated learning such as applications, challenges, privacy, function-alities, and design. With respect to the function and design of federatedlearning, client selection, aggregation, knowledge transfer, managementof distributed data (Non-IID), Incentive of data and communication costare of paramount importance. Any effective design of federated learningrequires these aspects to be well considered.There are numerous surveyarticles found among the available literature that focus on its applica-tion and challenges, opportunities, data privacy and protection, as wellas on federated learning on internet of things, federated learning on edgecomputing, etc.1 In this paper, a review of the available literature on the various ele-ments of design and functionalities in federated learning has been carriedout with an aim to lay emphasis on the important challenges andresearch opportunities. More specifically, this work has endeavored tounderstand and summarize the various functional methods available,along with their techniques and goals. Additionally, it has strived toget a bird’s eye view of how various functions and designs of feder-ated learning have been used in applications, and how it has helpeduncover challenges and promising research directions for the future.

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Energy efficient federated learning in internet of vehicles: A game theoretic scheme

Zhang

Wang

2023

Trans Emerging Tel Tech

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A promising alternative to conventional centralized machine learning that protects privacy is federated learning. Energy efficiency, meanwhile, appears as a major problem in federated learning for the Internet of Vehicles due to frequent model aggregations. To reduce the convergence time, existing research focuses on improving vehicular communication. However, it increases the computational and communication load on servers and disregards the true energy costs of vehicles. Thus, we propose an energy-efficient federated learning scheme based on two-stage game theories. Specifically, the payoff function first takes into account the energy cost as well as the contribution of each vehicle, which wins the fair payment based on the Shapley value via a cooperative game. Second, under the fairness constraint, energy-efficient vehicles cooperatively form several coalitions for further federated worker selection through multiple rounds of the hedonic game. Additionally, we also develop a multiagent reinforcement learning algorithm that is leveraged for coalition formation to lower the energy cost in the game. Furthermore, we demonstrate that the game in our scheme is Nash stable. Extensive experimental results show that the scheme improves energy efficiency by around 68.

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

Cited by 83 publications

References 134 publications

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Federated Learning Design and FunctionalModels: Survey

Energy efficient federated learning in internet of vehicles: A game theoretic scheme

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