Market Analysis of Distributed Learning Resource Management for Internet of Things: A Game-Theoretic Approach

Lee, Joohyung; Kim, Dae-Jin; Niyato, Dusit

doi:10.1109/jiot.2020.2991725

Cited by 44 publications

(8 citation statements)

References 25 publications

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“…This incentive process is modelled by a two-stage Stackelberg game that can outperform the heuristic approach in terms of a utility gain improvement by 22% for different system settings. The game theoretic approach is also adopted in [98] for the FL-based crowdsensing in IoT networks. MEC servers, cloud and IoT sensors cooperatively join to build a shared learning model in a fashion the utility of MEC operators is maximized by considering a tradeoff between the revenue and energy cost via a Stackelberg equilibrium formulation.…”

Section: E Fl For Iot Mobile Crowdsensingmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

693

182

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Section: E Fl For Iot Mobile Crowdsensingmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

693

182

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“…For each i P I and j P J , we define D i,j as the amount of dataset (i.e., the number of items in the dataset) determined by the MD i for MEC j. To simplify the scenario as in [23], we assume that all MDs have a sufficient D max of raw data that satisfies D i,j ăă D max owing to a sufficiently large value of D max (e.g., sensing information including GPS). The set of available D i,j for MD i corresponding to MEC j, which is denoted as D i,j , is a continuum given by D i,j " rD min , D max s, where D min can be provided by the MEC as the minimum requirement for participating in the FL task.…”

Section: B System Modelmentioning

confidence: 99%

“…7, the optimal strategy of each MEC (γ j ) increases as the number of MDs increases. This is because, to motivate them to contribute the global loss decay L decay,j , the MEC should try to sustain the per MD incentive during the increase in competition by increasing the total incentives by as much as (23). Moreover, as I max,j determined by each MEC increases, owing to the reduction of D i,j from the MDs, the total incentive γ j determined by the MEC is reduced to maximize its utility.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Novel Joint Dataset and Incentive Management Mechanism for Federated Learning Over MEC

2022

Self Cite

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“…Previous works on market analysis were confined to only one MEC operator. With a focus on latency reduction, Lee et al [65] proposed a distributed market model consisting of IoT devices, multiple MEC operators, and a cloud operator. They modeled the interaction among MECs and cloud using as Stackelberg game in which the MEC operators maximize their payoff by incorporating a tradeoff between revenue and energy consumption.…”

Section: B Game Theory Based Mechanismsmentioning

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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Market Analysis of Distributed Learning Resource Management for Internet of Things: A Game-Theoretic Approach

Cited by 44 publications

References 25 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

A Novel Joint Dataset and Incentive Management Mechanism for Federated Learning Over MEC

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

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