FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

Zeng, Rongfei; Zhang, Shixun; Wang, Jiaqi; Chu, Xiaowen

doi:10.1109/icdcs47774.2020.00094

Cited by 144 publications

(79 citation statements)

References 25 publications

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“…To address the problem of reluctance to actively participate in federated learning. Zeng et al [16] proposed a federated learning incentives mechanism based on multidimensional auctions. In this model, the server acts as a seller to send bid requests, and all users participate in the bidding.…”

Section: Incentive Mechanism In Federated Learningmentioning

confidence: 99%

“…Incentive mechanisms are an effective way to regulate conflict by rewarding users for submitting gradients with higher model accuracy. Common incentive mechanisms in federated learning include Stackelberg game-based methods [14,15] and auction-based methods [16]. Zhan et al [14] simulate a Stackelberg game model between servers and users to optimize the global cost of servers while satisfying user benefits.…”

Section: Introductionmentioning

confidence: 99%

“…Stackelberg game generally requires that one party to be the leader, so it is not appropriate for a status parity scenario. Unlike that, Zeng et al [16] propose an incentive mechanism based on the auction model. They evaluate and reward users based on their reputation and past behavior.…”

Section: Introductionmentioning

confidence: 99%

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Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Habib¹,

Kiani²,

Butt³

et al. 2022

Computers, Materials &Amp; Continua

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Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be compromised. b) When using differential privacy in combination with function encryption, the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible. In this paper, we propose a hybrid privacy-preserving scheme for federated learning, called Fed-DFE. Specifically, we present a decentralized multi-client function encryption algorithm. It replaces the TTP in traditional function encryption with an interactive key generation algorithm, avoiding the problem of collusion. Then, an embedded incentive mechanism is designed for function encryption. It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy. Subsequently, we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm. The experimental results demonstrate the effectiveness and efficiency of our scheme.

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Section: Incentive Mechanism In Federated Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Habib¹,

Kiani²,

Butt³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Zeng et al [78] proposed an incentive scheme "FMore" with multi-dimensional procurement auction for a multi-dimensional MEC setup. Their proposed approach has a low computational overhead and encourages high-quality data owners to participate in the training process at a low cost hence improving the training quality.…”

Section: E Auction 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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“…More than 50% of results may be influenced by the other factors [80], and therefore, it is important to consider the multiple dimensions of the data. For instance, Zend et al [56] brought a multi-dimensional incentive framework for FL to derive optimal strategies for edge devices, Jiao et al [57] designed a reverse multi-dimension auction system to better evaluate the value of each participant. The Nash equilibrium is a fundamental game theory model introduced by John Nash [81].…”

mentioning

confidence: 99%

Applying Federated Learning in Software-Defined Networks: A Survey

Liao

Zhi

et al. 2022

Symmetry

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Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

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FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

Cited by 144 publications

References 25 publications

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

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

Applying Federated Learning in Software-Defined Networks: A Survey

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