Federated Learning
Somanath Tripathy, Harsh Kasyap, Minghong Fang
Sold by Rarewaves USA United, OSWEGO, IL, U.S.A.
AbeBooks Seller since 20 June 2025
New - Hardcover
Condition: New
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by Rarewaves USA United, OSWEGO, IL, U.S.A.
AbeBooks Seller since 20 June 2025
Condition: New
Quantity: 1 available
Add to basketAs data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL's various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book's focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
Seller Inventory # LU-9781041174622
As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.
This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL’s various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.
This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.
This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book’s focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
Somanath Tripathy received his PhD from IIT Guwahati in 2007. Currently, he is a professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Patna, where he has been a faculty member since December 2008. Prof. Tripathy has held significant administrative positions at IIT Patna, including Associate Dean of Academics (January 2016 - March 2017), Head, Computer Centre (November 2022-November 2023) and Associate Dean of Administration (July 2021 - November 2023). His research interests encompass Cybersecurity, Malware Detection, Secure Machine Learning, Lightweight Cryptography, and Blockchain. Tripathy holds two patents and has published over 130 research papers in reputed journals and conferences. He has led several projects as Principal Investigator, notably his team developed a malware detection app presented to the Bureau of Police Research and Development (BPRD) and the Ministry of Home Affairs (MHA) as part of a sponsored project. Tripathy is currently an editor of the IETE Technical Review and an associate editor of the journal Multimedia Tools and Applications.
Harsh Kasyap is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (BHU), Varanasi, India. He is also an honorary research fellow at WMG, University of Warwick, UK. Prior to that, Harsh was a Research Associate, working in the Alan Turing Institute London, where he established significant research collaborations with the HSBC, Bank of Italy and TNO, advancing the fields of data privacy, AI security and fairness. He obtained his Ph.D. from the IIT Patna, India. His Ph.D. thesis title was “Security and Privacy Preserving Techniques for Federated Learning”. His research interests are Federated Learning, Machine Learning Security, Trustworthy AI, Privacy and Data Security.
Minghong Fang is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville. He was a Postdoctoral Associate in the Department of Electrical and Computer Engineering at Duke University from 2022 to 2024. He received his Ph.D. degree from the Department of Electrical and Computer Engineering at The Ohio State University in August 2022. He has published several high-impact research papers in top-tier security conferences, including the USENIX Security Symposium, the ACM Conference on Computer and Communications Security (CCS), and the Network and Distributed System Security (NDSS) Symposium. Notably, his USENIX Security 2020 paper was selected as one of the “Normalized Top-100 Security Papers Since 1981”. His research interests broadly span various aspects of AI safety and security.
"About this title" may belong to another edition of this title.
If you are a consumer you can cancel the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.
INFORMATION REGARDING THE RIGHT OF CANCELLATION
Statutory Right to cancel
You have the right to cancel this contract within 14 days for any reason.
The cancellation period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the the last good or the last lot or piece.
To exercise the right to cancel, you must inform us, Rarewaves USA United, 10100 W Sample Rd, Ste 101, 33065, Coral Springs, Florida, U.S.A., of your decision to cancel this contract by a clear statement (e.g. a letter sent by post, fax or e-mail). You may use the attached model cancellation form, but it is not obligatory. You can also electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". If you use this option, we will communicate to you an acknowledgement of receipt of such a cancellation on a durable medium (e.g. by e-mail) without delay.
To meet the cancellation deadline, it is sufficient for you to send your communication concerning your exercise of the right to cancel before the cancellation period has expired.
Effects of cancellation
If you cancel this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).
We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.
We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to cancel with contract.
We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.
We may withhold reimbursement until we have received the goods back or you have supplied evidence of having sent back the goods, whichever is the earliest.
You shall send back the goods or hand them over to us or Rarewaves USA United, 10100 W Sample Rd, Ste 101, 33065, Coral Springs, Florida, U.S.A., without undue delay and in any event not later than 14 days from the day on which you communicate your cancellation from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.
Exceptions to the right of cancellation
The right of cancellation does not apply to:
Model withdrawal form
(complete and return this form only if you wish to withdraw from the contract)
To: (Rarewaves USA United, 10100 W Sample Rd, Ste 101, 33065, Coral Springs, Florida, U.S.A.)
I/We (*) hereby give notice that I/We (*) withdraw from my/our (*) contract of sale of the following goods (*)/for the provision of the following goods (*)/for the provision of the following service (*),
Ordered on (*)/received on (*)
Name of consumer(s)
Address of consumer(s)
Signature of consumer(s) (only if this form is notified on paper)
Date
* Delete as appropriate.
Please note that we do not offer Priority shipping to any country.
We currently do not ship to the below countries:
Afghanistan
Bhutan
Brazil
Brunei Darussalam
Channel Islands
Chile
Israel
Lao
Mexico
Russian Federation
Saudi Arabia
South Africa
Yemen
Please do not attempt to place orders with any of these countries as a ship to address - they will be cancelled.
| Order quantity | 30 to 30 business days | 14 to 14 business days |
|---|---|---|
| First item | £ 36.99 | £ 49.57 |
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.