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Privacy-preserving Decentralized Learning Framework for Healthcare System

Published:14 June 2021Publication History
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Abstract

Clinical trials and drug discovery would not be effective without the collaboration of institutions. Earlier, it has been at the cost of individual’s privacy. Several pacts and compliances have been enforced to avoid data breaches. The existing schemes collect the participant’s data to a central repository for learning predictions as the collaboration is indispensable for research advances. The current COVID pandemic has put a question mark on our existing setup where the existing data repository has proved to be obsolete. There is a need for contemporary data collection, processing, and learning. The smartphones and devices held by the last person of the society have also made them a potential contributor. It demands to design a distributed and decentralized Collaborative Learning system that would make the knowledge inference from every data point. Federated Learning [21], proposed by Google, brings the concept of in-place model training by keeping the data intact to the device. Though it is privacy-preserving in nature, however, it is susceptible to inference, poisoning, and Sybil attacks. Blockchain is a decentralized programming paradigm that provides a broader control of the system, making it attack resistant. It poses challenges of high computing power, storage, and latency. These emerging technologies can contribute to the desired learning system and motivate them to address their security and efficiency issues. This article systematizes the security issues in Federated Learning, its corresponding mitigation strategies, and Blockchain’s challenges. Further, a Blockchain-based Federated Learning architecture with two layers of participation is presented, which improves the global model accuracy and guarantees participant’s privacy. It leverages the channel mechanism of Blockchain for parallel model training and distribution. It facilitates establishing decentralized trust between the participants and the gateways using the Blockchain, which helps to have only honest participants.

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          • Published in

            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 2s
            June 2021
            349 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/3465440
            Issue’s Table of Contents

            Copyright © 2021 Association for Computing Machinery.

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 14 June 2021
            • Revised: 1 September 2020
            • Accepted: 1 September 2020
            • Received: 1 July 2020
            Published in tomm Volume 17, Issue 2s

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