Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT

Fu, Xiuhua; Peng, Rongqun; Yuan, Wenhao; Ding, Tian; Zhang, Zhe; Yu, Peng; Kadoch, Michel

doi:10.3390/electronics12041034

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…The reliability assessment value of node N S i and block producer selection to reduce the energy consumption and improve the security of FL. [34] proposes an IoT resource management framework combining blockchain and FL, which uses support vector machine (SVM) classifier to improve the accuracy of detecting malicious nodes. [35] addresses the privacy risks of gradient inversion attacks by building a self-aggregating privacy-protecting FL model at the top of the blockchain.…”

Section: Rcmentioning

confidence: 99%

Network resource allocation method based on blockchain and federated learning in IoT

Zhi,

Wang

2024

J. Commun. Netw.

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Virtual network embedding (VNE) is an effective approach to solve the resource allocation problem in IoT networks. But most existing VNE methods are centralized methods, they not only impose an excessive burden on the central server but also result in significant communication overhead. Therefore, this paper proposes a distributed resource allocation method based on federated learning (DRAM-FL) to alleviate the computing and communication overhead, and improve network resource utilization. When utilizing DRAM-FL, it is essential to address the security challenges arising from the unreliable nature of IoT devices. So, we introduce blockchain into DRAM-FL, and propose a distributed resource allocation method based on blockchain and federated learning (DRAM-BFL). In DRAM-BFL, a dual-chain structure is designed to facilitate reliable information exchange among nodes, a node reliability assessment method and EPBFT-NRA consensus algorithm are proposed to improve the security of VNE. Simulation results demonstrate that, compared with other methods, DRAM-BFL can increase the VN acceptance rate and long-term average revenue-to-expenditure ratio while improving system security. In addition, DRAM-BFL exhibits good scalability, and has superior throughput and delay performance in IoT with malicious nodes.

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Section: Rcmentioning

confidence: 99%

Network resource allocation method based on blockchain and federated learning in IoT

Zhi,

Wang

2024

J. Commun. Netw.

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“…The derivative of the Sigmoid function is shown in Equation (11), and the derivative of the ReLU function is shown in Equation (12).…”

Section: Processing Of Nonlinear Operationsmentioning

confidence: 99%

“…For Equation (11), there is only one nonlinear function, the Sigmoid function. Therefore, the polynomial approximation of Equation ( 11) can be realized with the help of Equation ( 10) and expressed as…”

Section: Processing Of Nonlinear Operationsmentioning

confidence: 99%

“…However, there are two major issues in the FL framework. First, it is hard for the aggregators to check the validity of the updates uploaded by the trainers, which brings the risk of model poisoning attacks and reduces model availability [10][11][12]. The malicious trainers could launch model poisoning attacks by uploading invalid updates (for example, random float numbers generated randomly), which could interfere with the aggregation or convergence process, and reduce the global model accuracy.…”

Section: Introductionmentioning

confidence: 99%

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TrustDFL: A Blockchain-Based Verifiable and Trusty Decentralized Federated Learning Framework

Yang,

Zhang,

Guo

et al. 2023

Electronics

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Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained.

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“…They improved the Raft algorithm based on verifiable random functions and proposed a new consistency algorithm, V-Raft, which can quickly and stably select leaders to assist in the data sharing of VFL. After analyzing the key issues that need to be addressed in resource management in the future IoT, Fu et al [21] used federated learning to perform the distributed intelligent management of IoT resources and proposed an IoT resource management framework combining blockchain and federated learning. They employed Support Vector Machine (SVM) to detect malicious nodes and optimize the selection of participants in federated learning.…”

Section: Related Workmentioning

confidence: 99%

A Federated Learning Method Based on Blockchain and Cluster Training

Li,

Yan,

Liu

et al. 2023

Electronics

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Federated learning (FL) is an emerging machine learning method in which all participants can collaboratively train a model without sharing their raw data, thereby breaking down data silos and avoiding privacy issues caused by centralized data storage. In practical applications, client data are non-independent and identically distributed, resulting in FL requiring multiple rounds of communication to converge, which entails high communication costs. Moreover, the centralized architecture of traditional FL remains susceptible to privacy breaches, network congestion, and single-point failures. In order to solve these problems, this paper proposes an FL framework based on blockchain technology and a cluster training algorithm, called BCFL. We first improved an FL algorithm based on odd–even round cluster training, which accelerates model convergence by dividing clients into clusters and adopting serialized training within each cluster. Meanwhile, compression operations were applied to model parameters before transmission to reduce communication costs and improve communication efficiency. Then, a decentralized FL architecture was designed and developed based on blockchain and Inter-Planetary File System (IPFS), where the blockchain records the FL process and IPFS optimizes the high storage costs associated with the blockchain. The experimental results demonstrate the superiority of the framework in terms of accuracy and communication efficiency.

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Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT

Cited by 4 publications

References 30 publications

Network resource allocation method based on blockchain and federated learning in IoT

Network resource allocation method based on blockchain and federated learning in IoT

TrustDFL: A Blockchain-Based Verifiable and Trusty Decentralized Federated Learning Framework

A Federated Learning Method Based on Blockchain and Cluster Training

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