FedCoin: A Peer-to-Peer Payment System for Federated Learning

Liu, Yuan; Sun, Shuai; Ai, Zhengpeng; Zhang, Shuangfeng; Liu, Zelei; Yu, Han

doi:10.1007/978-3-030-63076-8_9

Cited by 97 publications

(75 citation statements)

References 13 publications

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“…The blockchain technologies are used to ensure the robustness and integrity of the shared information while remove the requirement of a central node. Blockchain-enabled FL can also be designed to encourage the data-owner participating in the model training process [ 104 ]. In addition, peer-to-peer protocols are also employed in FL to remove the need of a central node [ 105 , 106 ].…”

Section: Discussionmentioning

confidence: 99%

Federated Quantum Machine Learning

Chen

Yoo

2021

Entropy

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Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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Section: Discussionmentioning

confidence: 99%

Federated Quantum Machine Learning

Chen

Yoo

2021

Entropy

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“…Yet, the authors do not focus on the linkable and traceable transaction concerns in decentralized learning with blockchain-based incentives. Meanwhile, the research papers in [45], [46], [47], [48], [11], and [14] proposed a similar approach, but there is no information about the additional privacy in decentralized learning, nor about the unlinkable and untraceable transaction that can jeopardize the clients' identity and data.…”

Section: Comparative Analysismentioning

confidence: 99%

Unlinkable Collaborative Learning Transactions: Privacy-Awareness in Decentralized Approaches

Rahmadika

Rhee

2021

IEEE Access

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Smart contracts (SCs) and collaborative learning (CL) are disclosed publicly, in which most transactions and activities that occur by the parties can be bared in real-time. Both are strengthened in a decentralized manner. CL allows numerous clients to collectively build deep learning models privately by aggregating the gradient values from clients' devices, yet it lacks the incentive mechanism for the contributing clients. On the other hand, the merits of SCs can be a plausible solution as an incentive mechanism in the CL system because self-executing contracts with immutable data records are resistant to failure. The clients can claim the rewards by stating their contribution arbitrarily in the SCs and tendering a proof transaction function. Nevertheless, directly adopting SCs in the CL system could breach clients' privacy because the transactions are exposed openly. The observer can infer the properties of the clients' resources. Therefore, we designed schemes that can overcome observers' ability to link clients' information with their associated devices during training. In essence, our schemes are unbiased. We also provide a secure incentive mechanism for the parties in the CL system by obscuring the information values. Finally, the numerical results indicate that the proposed schemes satisfy the design goals.INDEX TERMS Blockchain, collaborative learning, decentralized approach, smart contracts, unlinkability.

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“…An important aspect of this problem is to determine a metric to quantify the contribution of EDs. SV-based schemes, as proposed by [72], are common in game theory to evaluate the contribution of EDs, however, it imparts extra cost to the server. To tackle this problem, Song et al [67] devised a new metric based on SV named Contribution Index (CI) for the valuation of contribution.…”

Section: B Game Theory Based Mechanismsmentioning

confidence: 99%

“…Liu et al [72] proposed a blockchain-based Peer-to-Peer (P2P) payment scheme called FedCoin. The incentives are provided using a blockchain network that works in connection to the FL network.…”

Section: F Blockchain 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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FedCoin: A Peer-to-Peer Payment System for Federated Learning

Cited by 97 publications

References 13 publications

Federated Quantum Machine Learning

Federated Quantum Machine Learning

Unlinkable Collaborative Learning Transactions: Privacy-Awareness in Decentralized Approaches

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

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