FLchain: Federated Learning via MEC-enabled Blockchain Network

Majeed, Umer; Hong, Choong Seon

doi:10.23919/apnoms.2019.8892848

Cited by 140 publications

(75 citation statements)

References 7 publications

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“…To support crowdsourcing in blockchain-enabled FL with high security, the authors in [70] proposed the ElGamal public key cryptographic scheme to protect the communication among federated blockchain worker and requester nodes. Blockchain has been used by researchers to monitor and control global model update and aggregation from local federated models [71] . The authors in [22] proposed an HE that could encrypt a batch of local model update gradients to lower communication and computational costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

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Section: Literature Reviewmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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“…With the tamper-resistant nature of blockchain, the behavior of participating nodes can be trusted, so that benign and malicious participating nodes can be identified and incentivized and punished accordingly. Majeed and Seon [7] proposed a new blockchain architecture. e concept of channels is used to learn multiple global models of this architecture.…”

Section: Related Workmentioning

confidence: 99%

DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems

Xuan

Jin

et al. 2021

Security and Communication Networks

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The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.

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“… Arooj et al (2020) proposed a route selection model using 4G internet technology in which smart vehicles are connected to the central system that can automatically select and alter their route depending on the type of signal they have received from the central system. Some researchers have integrated FL with block chain technology ( Kim & Hong, 2019 ; Farooq, Khan & Abid, 2020 ; Majeed & Hong, 2019 ; Huang et al, 2020 ; Weng et al, 2019 ). The FL system has been applied on synchronous training of algorithms ( Lim et al, 2019 ), vehicle-to-vehicle communications ( Samarakoon et al, 2018 ), medical applications ( Brisimi et al, 2018 ), and for earthquake early warning system using smart phones ( Kong et al, 2016 ).…”

Section: Related Workmentioning

confidence: 99%

A framework for the prediction of earthquake using federated learning

Tehseen

Farooq

Abid

2021

PeerJ Computer Science

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Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

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FLchain: Federated Learning via MEC-enabled Blockchain Network

Cited by 140 publications

References 7 publications

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems

A framework for the prediction of earthquake using federated learning

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