Federated Learning for IoT Applications PDF
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EAI/Springer Innovations in Communication and Computing Satya Prakash Yadav Bhoopesh Singh Bhati Dharmendra Prasad Mahato Sachin Kumar Editors Federated Learning for IoT Applications EAI/Springer Innovations in Communication and Computing Series Editor Imrich Chlamtac, European Alliance for Innovation, Ghent, Belgium Editor’s Note The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineering include, but are not limited to, wireless networks; mobile communication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution. Indexing: This series is indexed in Scopus, Ei Compendex, and zbMATH. About EAI EAI is a grassroots member organization initiated through cooperation between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellence by community. More information about this series at http://www.springer.com/series/15427 Satya Prakash Yadav Bhoopesh Singh Bhati Dharmendra Prasad Mahato • Sachin Kumar Editors Federated Learning for IoT Applications Editors Satya Prakash Yadav Bhoopesh Singh Bhati Department of Computer Science Department of Computer Science and Engineering and Engineering, Chandigarh University G.L. Bajaj Institute of Technology Mohali, India and Management (GLBITM) Affiliated to AKTU Sachin Kumar Lucknow, Uttar Pradesh, India Kyungpook National University Daegu, Korea (Republic of) Dharmendra Prasad Mahato National Institute of Technology Hamirpur Himachal Pradesh, India ISSN 2522-8595 ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-030-85558-1 ISBN 978-3-030-85559-8 (eBook) https://doi.org/10.1007/978-3-030-85559-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface Federated learning has become a new research brand in the field of IoT. Today, we see gigantic amounts data being generated by end devices such as mobile phones on a daily basis. These data contain valuable information about users and their personal preferences: what websites they mostly visit, which social media apps they gener- ally use, what types of videos they mostly watched, etc. With such valuable infor- mation, these data become the key to building better and personalized machine learning models to deliver personalized services to maximally enhance user experi- ences. Federated learning provides a unique way to build such personalized models without intruding users’ privacy. Such a unique advantage is the key motivation to attract researchers in the IoT community to work on this new research direction. Federated learning also opens up a brand-new computing paradigm for IoT. As compute resources inside end devices such as mobile phones are becoming increas- ingly powerful, especially with the emergence of IoT chipsets, IoT is moving from clouds and datacenters to end devices. Federated learning provides a privacy- preserving mechanism to effectively leverage those decentralized compute resources inside end devices to train machine learning models. Considering that there are billions of mobile devices worldwide, the compute resources accumulated from those mobile devices are way beyond the reach of the largest datacenter in the world. In this sense, federated learning has the potential to disrupt cloud computing, the dominant computing paradigm today. This book introduces deep understanding of federated learning and Internet of Things applications. The book explains the necessity of federated learning for IoT applications. It begins by providing a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. This book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intel- ligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneities in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. The book v vi Preface provides case studies of IoT-based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predic- tive control, linear matrix inequalities, and optimal control. This unique and com- plete co-design framework will greatly benefit researchers, graduate students, and engineers in the fields of control theory and engineering. Knowledge Park III, Greater Noida, Uttar Pradesh, India Satya Prakash Yadav Mohali, India Bhoopesh Singh Bhati Himachal Pradesh, India Dharmendra Prasad Mahato Daegu, Korea (Republic of) Sachin Kumar Contents Introduction to Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Mohit Pandey, Shubhangi Pandey, and Ajit Kumar Federated Learning for IoT Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Deena Nath Gupta, Rajendra Kumar, and Ashwani Kumar Personalized Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Kaushal Kishor Federated Learning for an IoT Application . . . . . . . . . . . . . . . . . . . . . . . . . 53 Deena Nath Gupta, Rajendra Kumar, and Shamsul Haque Ansari Some Observations on the Behaviour of Federated Learning . . . . . . . . . . 67 Vishal Kaushal and Sangeeta Sharma Federated Learning with Cooperating Devices: A Consensus Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Radhika Vadhi and Abhishek Sharma A Prospective Study of Federated Machine Learning in Medical Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Rijwan Khan, Mahima Gupta, Pallavi Kumari, and Narendra Kumar Communication-Efficient Federated Learning in Wireless-Edge Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Sugandh Gupta and Sapna Katiyar Communication-Efficient Federated Learning . . . . . . . . . . . . . . . . . . . . . . 135 Kaushal Kishor Federated Learning Using Tensor Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Tanu Solanki, Bipin Kumar Rai, and Shivani Sharma vii viii Contents Cyber Security and Privacy of Connected and Automated Vehicles (CAVs)-Based Federated Learning: Challenges, Opportunities, and Open Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Naziya Hussain, Preeti Rani, Harsha Chouhan, and Urvashi Sharma Gaur Security Issues and Solutions for Healthcare Informatics . . . . . . . . . . . . . 185 Bipin Kumar Rai Federated Learning: Challenges, Methods, and Future Directions . . . . . . 199 Pushpa Singh, Murari Kumar Singh, Rajnesh Singh, and Narendra Singh Quantum Federated Learning for Wireless Communications . . . . . . . . . . 215 R. M. Pujahari and Akshit Tanwar Federated Machine Learning with Data Mining in Healthcare . . . . . . . . . 231 Nitesh Singh Bhati, Garvit Chugh, and Bhoopesh Singh Bhati Federated Learning for Data Mining in Healthcare . . . . . . . . . . . . . . . . . . 243 Shivani Sharma, Akash Kesarwani, Shreyshi Maheshwari, and Bipin Kumar Rai Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Introduction to Federated Learning Mohit Pandey, Shubhangi Pandey, and Ajit Kumar 1 Introduction Recently, machine learning (ML)-based technologies has grown in various artificial intelligence (AI) applications, such as image recognition, medical imaging, speech recognition, pattern recognition, and natural language processing [1–3]. Day by day, the amount of data increases. To handle this, a vast amount of data machine learning-based technologies is required. This has paved way for successes of machine leaning [2, 4]. The art of deep learning has made human life easy with the use of these human- or machine-generated data. Deep learnings-based application performs automatic face recognition systems, automatic segmentation of human organs in medical images like computed tomography images and ultrasound, etc. Any deep learning-based system requires a large amount of data to achieve signifi- cant results. For instance, Facebook reported that 3.5 billion images is required for their object detection application. Internet of things utilizes benefits of edge computing, in which high amount of data is generated. That data is not considered as a single big data but is rather dis- tributed among many sites. For instance, a single big amount of data transmission is difficult, and of course it is not a good idea, like Earth’s image taken by satellites. M. Pandey (*) School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India S. Pandey Department of Computer Application, ABES Engineering College, Ghaziabad, UP, India A. Kumar School of Computer Science and Engineering Soongsil University, Dongjak-gu, Seoul, South Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 1 S. P. Yadav et al. (eds.), Federated Learning for IoT Applications, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-85559-8_1
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