Profit Allocation for Federated Learning

Song, Tianshu; Tong, Yongxin; Wei, Shuyue

doi:10.1109/bigdata47090.2019.9006327

Cited by 121 publications

(77 citation statements)

References 10 publications

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“…T. Song et al propose the Federated Learning Profit Allocation (FLPA) scheme that focuses on the scenario of horizontal enterprise FL [11]. Based on the concept of Shapley value, they define formally the Contribution Index (CI) of different data providers in a FL task, and propose two efficient methods to calculate the CIs.…”

Section: Related Workmentioning

confidence: 99%

“…The key idea of these two methods is that authors only need to records intermediate results during the training process of federated learning and use these intermediate results to calculate the CIs approximately. Finally, they conduct extensive experiments on different setting to verify the effectiveness and efficiency of the proposed methods [11].…”

Section: Related Workmentioning

confidence: 99%

“…Although some researches have exploited extensively the FL control paradigm, an efficient cooperation of privacysensitive IoT devices has not been fully investigated. Different from the existing HIFL, SIFL and FLPA protocols [1], [7], [11], our DP-preserving FL approach can make rational decisions in a cooperative manner while effectively ensuing mutual advantages; it has more potential benefits for the distributed FL paradigm.…”

Section: Related Workmentioning

confidence: 99%

“…Step 3: In each device, its valuation function (•), and utility function are defined according to Eq. (11) and Eq. (12).…”

Section: The Proposed Dp-based Vcg Mechanism For Federated Learningmentioning

confidence: 99%

“…In this section, we evaluate the performance of our proposed scheme and report the experimental results while comparing with other existing HIFL, SIFL and FLPA schemes [1], [7], [11]; these existing schemes are recently published state-of-the-art FL protocols. First, we describe the experiment settings and simulation scenario, and then present the numerical analysis to draw insights for validation.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim

2020

IEEE Access

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Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently. FL is capable of significantly preserving end-users' private data from being exposed to external adversaries. However, private information can still be divulged by uploading parameters from users. Therefore, a key challenge in the FL platform is how users participate to build a high-quality learning model with effectively preventing information leakage. To address the above challenge, we design a novel incentive mechanism to attract more data owners to join in the FL process with the consideration of privacy preservation. To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives, and the DP can provide a theoretical guarantee for users' privacy in FL participations. Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators. Therefore, we can take various benefits in a rational way under the dynamic changing FL environments. Through simulation analysis, the numerical results validate the performance efficiency of our proposed scheme.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Step 3: In each device, its valuation function (•), and utility function are defined according to Eq. (11) and Eq. (12).…”

Section: The Proposed Dp-based Vcg Mechanism For Federated Learningmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim

2020

IEEE Access

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CAreFL: Enhancing smart healthcare with Contribution‐Aware Federated Learning

et al. 2023

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Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications.Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution-Aware Federated Learning (CAreFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants' contributions to model performance without exposing their private data, and improves the FL model training protocol by allowing the best performing intermediate sub-models to be distributed to participants for FL training. Since its deployment by Yidu Cloud Technology Inc. in March 2021, CAreFL has served eight well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations close to three times faster than the best existing approach and has improved the average accuracy of the resulting models by more than 2% compared to the previous system (which is significant in industrial settings). To the best of our knowledge, it is the first CAreFL successfully deployed in the healthcare industry.

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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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Profit Allocation for Federated Learning

Cited by 121 publications

References 10 publications

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

CAreFL: Enhancing smart healthcare with Contribution‐Aware Federated Learning

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

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