Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm

Qu, Xidi; Wang, Shengling; Hu, Qin; Cheng, Xiuzhen

doi:10.1109/tpds.2021.3056773

Cited by 84 publications

(40 citation statements)

References 19 publications

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“…is method mainly solves the problem of information asymmetry when the two subjects participate in emission reduction. For example, Qu et al [6] believe that core enterprises usually cooperate with noncore enterprises to jointly improve corporate social responsibility awareness (including environmental responsibility awareness). Building a principal-agent model can solve the problem of how to improve corporate social responsibility when there is information asymmetry between core enterprises and noncore enterprises.…”

Section: Related Workmentioning

confidence: 99%

“…As a tamper proof, transparent, and reliable database, blockchain has an impact on the green operation of the supply chain [5]. e application of blockchain technology can realize information sharing, improve the collaborative operation level and operation efficiency of the supply chain, and reduce the period cost [6,7]. In addition, blockchain technology can realize product traceability and carbon footprint tracking and can effectively solve the problem of green trust of consumers in purchasing green and low-carbon products [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

Yin

2021

Mobile Information Systems

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This paper takes the secondary low-carbon supply chain composed of a manufacturer and a retailer as the research object and takes the government subsidy strategy of low-carbon supply chain as the research theme, the government invests in the construction of blockchain technology application platform, and supply chain enterprises use blockchain technology for compensation. Under this background, considering consumers’ low-carbon preference and green trust, a three-stage game model dominated by the government and followed by manufacturers and retailers under the four scenarios of emission reduction technology input subsidy and low-carbon product output subsidy before and after the application of blockchain technology is established. By comparing the optimal social welfare, carbon emission reduction rate, and low-carbon product output under the four scenarios, the optimal problem of government low-carbon subsidy strategy is discussed. It is found that when the product of consumers’ low-carbon preference coefficient and green trust coefficient is greater than a certain fixed value, the government can obtain higher social welfare and promote enterprises’ emission reduction through output subsidies. The government can always promote the demand for low-carbon products through output subsidies. When the emission reduction cost coefficient approaches infinity, the demand for low-carbon products when the government implements output subsidies are four times that when the government implements technology subsidies. When the single cycle shared cost of the blockchain platform and the unit cost of enterprise application of blockchain technology are less than a certain threshold, the government’s construction of the blockchain platform can obtain higher social welfare, promote enterprise emission reduction, and improve consumers’ demand for low-carbon products.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

Yin

2021

Mobile Information Systems

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“…In [100], a novel consensus mechanism, proof of FL, is proposed, in which the pool members are the buyers to cooperatively train a global model from the pool manager based on their privacy data purchased from the data providers (i.e., sellers). To guard against the risk of privacy data leakage in FL, a reverse game-based data trading mechanism is proposed which enables a pool member (miner) to maximize its utility only when it trains the model without any leakage.…”

Section: Stable Cluster Formation and Budget Balancementioning

confidence: 99%

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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“…Instead, it makes full use of the local network data to assist in training the detection model. Accordingly, the overall performance of the detection model can be improved, and the privacy data leakage problem can be avoided in the process of data transmission [13].…”

Section: Intrusion Detection Model Based On Federated Learningmentioning

confidence: 99%

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

Xie

Dong

Wang

2021

Wireless Communications and Mobile Computing

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The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

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Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm

Cited by 84 publications

References 19 publications

Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm

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

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

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