Lixin Fan - Böcker
Visar alla böcker från författaren Lixin Fan. Handla med fri frakt och snabb leverans.
5 produkter
5 produkter
Del 12500 - Lecture Notes in Computer Science
Federated Learning
Privacy and Incentive
Häftad, Engelska, 2020
940 kr
Skickas inom 10-15 vardagar
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications.
Del 13448 - Lecture Notes in Computer Science
Trustworthy Federated Learning
First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
Häftad, Engelska, 2023
593 kr
Skickas inom 10-15 vardagar
This book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.
Digital Watermarking for Machine Learning Model
Techniques, Protocols and Applications
Inbunden, Engelska, 2023
1 855 kr
Skickas inom 10-15 vardagar
Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model’s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications
Engelska, 2023
612 kr
Skickas inom 5-8 vardagar
Digital Watermarking for Machine Learning Model
Techniques, Protocols and Applications
Häftad, Engelska, 2024
1 855 kr
Skickas inom 10-15 vardagar
Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR).