Co-Utile Peer-to-Peer Decentralized Computing

Domingo-Ferrer, Josep; Blanco-Justicia, Alberto; Sánchez, David; Jebreel, Najeeb Moharram

doi:10.1109/ccgrid49817.2020.00-90

Cited by 9 publications

(8 citation statements)

References 24 publications

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“…Ferrer et al [55] employed co-utility to tackle the problem of peer honesty. They study decentralized computing in two cases: (i) peers are provided with a task along with the data, and (ii) peer use their data to train a model.…”

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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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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“…We show that on top of the unique formal guarantees of Section 6.2, both protocols are cost-effective and practical for both computers and requesters. Our reputation and computer selection mechanisms perform analogously to those graphed in [8]. Crucially however, our protocols are proven to be strong incentive compatible in a fully decentralized setting, where computers an requesters have asymmetric utilities.…”

Section: Implementation Analysismentioning

confidence: 93%

“…As these reputations are maintained on the blockchain itself, through careful construction (Section 5.2) this reputation protocol neither affects the decentralisation or strong incentive compatibility of the protocol. When using a fixed number of computers per computation, our protocol shares all of the benefits of [8]. Our description of Marvel DC can be adapted to run on any smart-contract enabled blockchain, and as such, can make use of the vast existing communities which exist on such blockchains.…”

Section: Our Contributionmentioning

confidence: 99%

“…Our protocol uses a decentralized pseudorandom function (such as a verifiable on-chain oracles 3 , or those used in [5]) to provide a random seed used to select computers based on reputation. This removes the possibility of reputation-based manipulation by clusters of colluding computers who may try to prioritize the selection of computers in the colluding cluster, as is allowed in existing works [8,7,24].…”

Section: Our Contributionmentioning

confidence: 99%

“…8 https://docs.chain.link/docs/chainlink-vrf/ tional contract, Reveal, which is to be executed after the Response contract, and before rewards are finalized. The purpose of the Reveal contract is described later in this section.During computer registration, computers in Privacy Marvel DC privately generate S 1 , r 1 ∈ {0, 1} O(κ) , and attach regID 1 ← f com (S 1 , r 1 ) to the registration message, as described in Section 4.2.…”

mentioning

confidence: 99%

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Marvel DC: A Blockchain-Based Decentralized and Incentive-Compatible Distributed Computing Protocol

McMenamin¹,

Daza²

2022

Preprint

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Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things.Unfortunately, when trusted third parties are removed to achieve this decentralization, ensuring an outsourced computation is performed correctly remains a significant challenge. In this paper, we provide a solution to this problem.We outline Marvel DC, a fully decentralized blockchain-based distributedcomputing protocol which formally guarantees that computers are strictly incentivized to correctly perform requested computations. Furthermore, Marvel DC utilizes a reputation management protocol to ensure that, for any minority of computers not performing calculations correctly, these computers are identified and selected for computations with diminishing probability. We then outline Privacy Marvel DC, a privacy-enhanced version of Marvel DC which decouples results from the computers which computed them, making the protocol suitable for computations such as Federated Learning, where results can reveal sensitive information about that computer that computed them. We provide an implementation of Marvel DC and analyses of both protocols, demonstrating that This document is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 814284

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Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems

Dirir

Svetinovic

2021

Federated Learning Systems

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Co-Utile Peer-to-Peer Decentralized Computing

Cited by 9 publications

References 24 publications

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

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

Marvel DC: A Blockchain-Based Decentralized and Incentive-Compatible Distributed Computing Protocol

Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems

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