Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Adversarial AI and Data Poisoning in Federated Learning

Adversarial AI and Data Poisoning in Federated Learning

Jain, Vipul
Khullar, Shikha
Joshi, Manju Lata

Paperback

General ComputersComputer Security

Publisher Price: $235.00

ISBN13: 9798337362250
Publisher: Igi Global Scientific Publishing
Published: Feb 20 2026
Pages: 574
Weight: 2.89
Height: 1.16 Width: 8.50 Depth: 11.00
Language: English
With the growing security challenges at the intersection of distributed machine learning and malicious interference, there are growing challenges that federated learning can address. Federated learning enables collaborative model training across devices while preserving data privacy. However, this decentralized nature also opens new vulnerabilities, particularly to adversarial attacks and data poisoning, where malicious actors can inject corrupted data or manipulate updates to degrade models or extract sensitive information. As the adoption of federated learning accelerates, understanding and these threats are essential to ensure model integrity and resilience in real-world situations. Adversarial AI and Data Poisoning in Federated Learning provides a comprehensive examination of emerging threats, attack vectors, and defense mechanisms within federal learning systems. This book highlights vulnerabilities of federated learning architectures, explores strategies for detection and mitigation of adversarial threats, and presents real-world case studies.

Also from

Jain, Vipul

View All

Also in

General Computers

View All