[hpc-announce] Call for Chapters - Federated Learning: Foundations and Applications

[Rajkumar Buyya]

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         Federated Learning: Foundations and Applications

Edited by: Rajkumar Buyya, Anwesha Mukherjee, Sajal K. Das
Publisher: Elsevier, USA (proposed)

           A Call for Book Chapters
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The Society 5.0 paradigm has focused on the integration of technology 
with the different aspects of societal applications including 
healthcare, agriculture, retail, and transportation. Internet of Things, 
cloud computing, edge computing, and data analytics are integrated to 
provide smart solutions for the society. The machine learning and deep 
learning algorithms are used for data analysis inside the cloud servers. 
However, the entire data transmission and storage inside the cloud may 
raise several issues such as high network traffic, high latency, 
security concerns, etc. Further, the collaborative learning is 
significant for accurate prediction. Federated learning has come with 
the solutions of these challenges. The use of federated learning with 
edge/fog computing can result in efficient data analysis in terms of 
accuracy, latency, etc.

Federated learning is a collaborative model training approach where the 
local models are used for analyzing local data, and the model parameters 
are exchanged between the clients and servers for updating the model. 
There are two types of federated learning are well-known: centralized 
and decentralized federated learning. In centralized federated learning, 
a global model is maintained inside the server that is updated based on 
the reception of local model parameters from the clients. The updated 
model is shared with the clients. In decentralized federated learning, 
the clients form a peer-to-peer network and share model parameters among 
themselves for collaborative training and updating the model 
accordingly. The local data analysis enhances data privacy protection. 
Further, the collaborative learning improves prediction accuracy. 
Moreover, the response time is improved. The energy-efficiency is also 
achieved using federated learning-based models.

The book will cover the foundations, architectures and systems, security 
and privacy, and applications of federated learning. Part-I will cover 
the fundamentals aspects of federated learning including machine 
learning, deep learning, centralized learning and distributed learning 
processes. Part-II will cover the architectures, algorithms and system 
models of federated learning. Part-III will focus on the security, 
privacy, and energy-efficiency in federated learning. Finally, part-III 
will explore the different application areas of federated learning.





Some of the key topics to be covered in this book are as follows:

Topics: (Tentative and more to be added)

1) Foundations and Principles
•	Centralized and decentralized federated learning
•	Machine learning, deep learning, centralized to distributed learning

2) Architecture and Systems
•	Centralized federated learning (CFL) algorithms
•	Decentralized federated learning (DFL) algorithms
•	Gossip learning
•	Deterministic and probabilistic design methodologies for federated 
learning

3) Security, Privacy, and Energy-efficiency
•	Security and privacy in federated learning
•	Homomorphic encryption and federated learning
•	Blockchain in federated learning
•	Energy-aware federated learning models
•	Latency-aware federated learning models
•	Mobility-aware federated learning

  4) Applications
•	Federated learning in 6G
•	Federated learning for society 5.0
•	Federated learning for sustainable IoT applications including 
healthcare, transportation, agriculture

Important Dates – Proposed
Chapter Proposal: You are invited to submit a 1-2 pages Proposal with a 
brief description of the topic of the chapter. The proposal should 
include the chapter organization, anticipated number of pages of the 
final manuscript and brief biography of the authors.

The proposed timeline to be followed is:
•	Proposal deadline: August 30, 2024 (Early expression of interest is 
highly encouraged)
•	Notification of proposal acceptance: September 16, 2024
•	Full draft chapter submission: December 10, 2024
•	Chapter review report to authors: Jan 30, 2025
•	Final version submission: Feb 15, 2025

Early submission is highly appreciated as the editors would like to have 
progressive dialogue and work with prospective authors to bring out a 
book of wide appeal.
Please submit your proposal in PDF / Word format by email to:
anweshamukherjee2011 at gmail.com with CC to other editors.

If we receive more than one proposal for a chapter on the same topic, 
the editors may request authors to collaborate to develop an integrated 
chapter.

Manuscript Submission
Each accepted chapter should have about 20-35 A4 pages. We expect to 
deliver CRC of the book to the publisher. A MS Word template will be 
provided later.


Editors:

•	Professor Rajkumar Buyya
Director, Cloud Computing and Distributed Systems (CLOUDS) Lab
School of Computing and Information Systems
The University of Melbourne, Australia
Email: rbuyya at unimelb.edu.au

•	Dr. Anwesha Mukherjee
Assistant Professor,
Department of Computer Science,
Mahishadal Raj College (Vidyasagar University),
Mahishadal, West Bengal, 721628, India.
Email: anweshamukherjee2011 at gmail.com

•	Dr. Sajal Das
Curators' Distinguished Professor & Daniel St. Clair Endowed Chair,
Computer Science,
Missouri University of Science and Technology
Email: sdas at mst.edu
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