A Multi-player Game for Studying Federated Learning Incentive Schemes

Ng, Kang Loon; Chen, Zichen; Liu, Zelei; Yu, Han; Liu, Yang; Yang, Qiang

doi:10.24963/ijcai.2020/769

Cited by 26 publications

(12 citation statements)

References 1 publication

(8 reference statements)

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“…In FL, Shapley value might has a broad application such as contribution measurement of participant [22], [24], [25], participant behaviour analysis [23], and feature selection [54]. Especially, Ng et al implement multiple payoff-sharing schemes (including Shapley value) in a visual tool of a multiplayer game to study how FL participants act under different incentive schemes through crowdsourcing in [23].…”

Section: A Shapley Valuementioning

confidence: 99%

A Comprehensive Survey of Incentive Mechanism for Federated Learning

Zeng,

Wang

et al. 2021

Preprint

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Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.

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Section: A Shapley Valuementioning

confidence: 99%

A Comprehensive Survey of Incentive Mechanism for Federated Learning

Zeng,

Wang

et al. 2021

Preprint

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“…Ng et al [53] studied the behavior of FL participants under certain incentive schemes. They propose FedGame, a multiplayer game to analyze how FL participants make decisions under different incentive schemes through crowdsourcing.…”

Section: G a Discussion On Partially-related Non-selected Articlesmentioning

confidence: 99%

Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review

Ali¹,

Ilahi²,

Qayyum³

et al. 2021

Preprint

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In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine (ML) learning models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. According to the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that required further research attention.

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“…Currently, FL has been integrated with other emerging technologies by many scholars to enable industrial applications, such as the efficiency improvement of mobile and wireless communication (Konecný et al 2016;Sattler et al 2020;Reisizadeh et al 2020;Niknam et al 2020), edge computing Doku et al 2021;Fantacci and Picano 2020;, health care (Rieke et al 2020;Bogdanova et al 2020;Zerka et al 2020), Internet of Things (Savazzi et al 2020;Yang et al 2020;Yuan et al 2020;Qolomany et al 2020;Briggs et al 2020;Gao et al 2020;Kamel and Mougy 2020;Imteaj and Amini 2019), Internet of Vehicles (Samarakoon et al, 2020;Hsu et al, 2020;, anomaly detection (Nguyen et al 2019;Weinger et al 2020), smart city (Jiang et al 2020), financial fraud identification (Fan et al 2020), visual object detection ) and fog computing ). It can be seen that FL is prominent in industrial applications for privacy-sensitive data and the processing of non-IID data.…”

Section: Applications Of Flmentioning

confidence: 99%

“…Some studies have already begun to try the node incentive of FL, such as Ng et al (2020), Khan et al (2020). However, since there is no actual token mechanism design, these studies mainly focus on documentation, detection, and simulation.…”

Section: Handling Lazy Clientsmentioning

confidence: 99%