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Deniable Authentication Encryption for Privacy Protection using Blockchain
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Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks
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Deniable Authentication Encryption for Privacy Protection using Blockchain
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Real Time Anomaly Detection Techniques Using PySpark Frame Work
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Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
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Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
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Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
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Frontiers of AI beyond 2030: Novel Perspectives
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Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
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Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
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An Efficient Machine Learning based Model for Classification of Wearable Clothing
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Volume - 3 | Issue - 3 | september 2021

Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Shajulin Benedict  , Deepumon Saji, Rajesh P. Sukumaran, M. Bhagyalakshmi  442  199
Pages: 196-217
Cite this article
Benedict, S., Saji, D., Sukumaran, R. P. & Bhagyalakshmi, M. (2021). Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications. Journal of Artificial Intelligence and Capsule Networks, 3(3), 196-217. doi:10.36548/jaicn.2021.3.004
Published
30 August, 2021
Abstract

The biggest realization of the Machine Learning (ML) in societal applications, including air quality prediction, has been the inclusion of novel learning techniques with the focus on solving privacy and scalability issues which capture the inventiveness of tens of thousands of data scientists. Transferring learning models across multi-regions or locations has been a considerable challenge as sufficient technologies were not adopted in the recent past. This paper proposes a Blockchain- enabled Federated Learning Air Quality Prediction (BFL-AQP) framework on Kubernetes cluster which transfers the learning model parameters of ML algorithms across distributed cluster nodes and predicts the air quality parameters of different locations. Experiments were carried out to explore the frame- work and transfer learning models of air quality prediction parameters. Besides, the performance aspects of increasing the Kubernetes cluster nodes of blockchains in the federated learning environment were studied; the time taken to establish seven blockchain organizations on top of the Kubernetes cluster while investigating into the federated learning algorithms namely Federated Random Forests (FRF) and Federated Linear Regression (FLR) for air quality predictions, were revealed in the paper.

Keywords

Air Quality Blockchain Federated Learning Kubernetes Smart cities

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