Privacy-preserving model training architecture for intelligent edge computing

Qu, Xidi; Hu, Qin; Wang, Shengling

doi:10.1016/j.comcom.2020.07.045

Cited by 21 publications

(6 citation statements)

References 8 publications

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“…Case2 : µi qti p ti + ψ qmi p mi − ρ i µ i q 2 ti − ρ i ψq 2 mi = 0, ψ qti p ti − R ti = 0, α 2 = 0, ∀i. By solving the above equations, we get (19) and (20). We can verify that the solutions above is incentive compatible and satisfy all the KKT conditions.…”

Section: Appendix C Proof Of Theorem 41mentioning

confidence: 94%

“…To guarantee the fairness of reward distribution, we allocate rewards based on the contribution of each client in the training process. Considering that Shapely Value (SV) [19] is a methodology which can distribute the rewards to participants according to their respective contributions, here we apply it to facilitate reward distribution. The SV of client i is defined as…”

Section: Upper Bound Of Rewards For Trainingmentioning

confidence: 99%

“…Training and mining are two different tasks that require different amount of computational power, and thus the rewards should also be different. To ensure a fair distribution of rewards to all clients, we employ the approach of Shapely Value (SV) [19] to determine the contributions of clients in the training process, which will affect the constraints in their respective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

Wang¹,

Hu²,

Li³

et al. 2022

Preprint

Self Cite

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Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages such as decentralization and privacy protection of raw data. However, there has been few research focusing on the allocation of resources for clients in BCFL. In the BCFL framework where the FL clients and the blockchain miners are the same devices, clients broadcast the trained model updates to the blockchain network and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the model owner (MO) (i.e., the BCFL task publisher) and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.

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Section: Appendix C Proof Of Theorem 41mentioning

confidence: 94%

Section: Upper Bound Of Rewards For Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

Wang¹,

Hu²,

Li³

et al. 2022

Preprint

Self Cite

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“…Poularakis et al studied the joint optimization of service placement and request routing in MECenabled multi-cell networks with storage-computationcommunication constraints [33]. Data security and privacy protection in the field of edge computing have also attracted the attention of many scholars [34,35]. The integration of blockchain and edge computing is becoming an important concept that leverages their decentralized management and distributed service to meet the security, privacy protection, scalability and performance requirements in future networks and systems [36].…”

Section: Related Workmentioning

confidence: 99%

Joint optimization of network selection and task offloading for vehicular edge computing

Tang

Zhang

et al. 2021

J Cloud Comp

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Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

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“…3) Shapley-Value based Schemes: Qu et al [64] designed a two-phase ML framework for the incentivization of edge servers in a cloud-edge-device cooperative manner. The first phase deals with the FL process between cloud and edge servers while the second phase deals with global model segmentation for personalized requirements.…”

Section: B Game 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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Privacy-preserving model training architecture for intelligent edge computing

Cited by 21 publications

References 8 publications

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

Joint optimization of network selection and task offloading for vehicular edge computing

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

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