Table Of ContentEAI/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
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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
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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
