Cybersecurity vulnerabilities in federated learning

Title

Cybersecurity vulnerabilities in federated learning

Creator

Haseena, Shaik Valli; Shanavas, Simna; Brundha, N.; Ayasha

Description

Federated Learning (FL) has been conceived as a dispersed machine learning paradigm facilitating collaborative learning at edge devices without exposing raw data. The model is amenable to privacy preservation and data protection regulation, for example, General Data Protection Regulation compliance. Yet, more widespread deployment of FL reveals a new and extreme spectrum of cybersecurity risks. These consist of data poisoning attacks that can potentially severely contaminate model integrity, model inversion attacks that can potentially recover sensitive data from exchanged gradients, adversarial manipulations where malicious agents take advantage of model weaknesses, and incidental privacy leakage. The impact and real world implication of these attacks differs, for example, a successful poisoning attack in medicine can result in misdiagnosis, model inversion in the finance sector could leak client confidential data, and adversarial attacks in Internet of Things (IoT) would control autonomous devices with safety consequences. This chapter critically reviews these threats taking into consideration attack feasibility, harm extent, and detectability, inspired by recent case studies illustrating their applicability in real world FL deployments. We also analyze the effectiveness of current state of the art countermeasures like robust aggregation methods, differential privacy, and cryptographic methods like secure multiparty computation and homomorphic encryption. By synthesizing current research on attack paradigms and counterattack architectures, the chapter offers practical knowledge towards constructing secure, robust, and trustworthy FL systems, particularly in high-risk applications like medicine, finance, and critical infrastructure. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors.

Source

Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI;pp.61-86

Date

01-01-2026

Publisher

CRC Press

Coverage

Haseena S.V., Presidency College, Bengaluru, India; Shanavas S., Presidency College, Bengaluru, India; Brundha N., M S Ramaiah College of Arts Science and Commerce, Bangalore, India; Ayasha, Christ College of Science and Management, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-104086966-6; 978-104111510-6;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003660330-4

https://www.scopus.com/pages/publications/105035266396?origin=resultslist