Federated Learning Defenses – Bücher zum Thema Federated Learning Defenses finden und kaufen

Handbook of Trustworthy Federated Learning

Springer International Publishing
235.39 € · Hardcover

optimization federated learning trustworthy federated learning differential privacy privacy-preserving certified defenses graph neural networks

This handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on federated learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various to...

Security and Resilience in Distributed Machine Learning

Kai Li, Xin Yuan, Wei Ni
Springer International Publishing
192.59 € · Hardcover

Reliable Federated Learning Federated Learning Security AI Security and Robustness Federated Learning Attacks Federated Learning Defenses Data Privacy in AI Resilient Federated Learning

This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrit...

Robust AI: Security and Privacy Issues in Machine Learning

Nandish Chattopadhyay, Anupam Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
Springer Singapore
213.99 € · Hardcover

Watermarking Neural Networks Robust Federated Learning AI Security Robust Machine Learning Adversarial Attacks And Defences Protecting Privacy of Training Data Protecting IP of Trained Models Efficient Decision-based Adversarial Attacks Pre-Processing based Defenses Patch-based real-world adversarial attacks

This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing with vulnerabilities where the errors or malfunctions are both intentional and malicious, therefore being associated with a specific attack model. Reliability is key to the wider adoption of machine le...

Handbook of Trustworthy Federated Learning

Springer International Publishing
235.39 € · Paperback

optimization federated learning trustworthy federated learning differential privacy privacy-preserving certified defenses graph neural networks

This handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on federated learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various to...

Handbook of Trustworthy Federated Learning

Springer International Publishing
235.39 € · eBook

optimization federated learning trustworthy federated learning differential privacy privacy-preserving certified defenses graph neural networks

This handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on federated learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various to...

Security and Resilience in Distributed Machine Learning

Kai Li, Xin Yuan, Wei Ni
Springer International Publishing
192.59 € · eBook

Reliable Federated Learning Federated Learning Security AI Security and Robustness Federated Learning Attacks Federated Learning Defenses Data Privacy in AI Resilient Federated Learning

This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrit...

Robust AI: Security and Privacy Issues in Machine Learning

Nandish Chattopadhyay, Anupam Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
Springer Singapore
€ · eBook

Watermarking Neural Networks Robust Federated Learning AI Security Robust Machine Learning Adversarial Attacks And Defences Protecting Privacy of Training Data Protecting IP of Trained Models Efficient Decision-based Adversarial Attacks Pre-Processing based Defenses Patch-based real-world adversarial attacks

This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing with vulnerabilities where the errors or malfunctions are both intentional and malicious, therefore being associated with a specific attack model. Reliability is key to the wider adoption of machine le...

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