• PDF / 28,472,924 Bytes
• 291 Pages / 439.37 x 666.142 pts Page_size
• 84 Downloads / 196 Views

DOWNLOAD

REPORT

State-of-the-Art Survey

Qiang Yang Lixin Fan Han Yu (Eds.)

Federated Learning Privacy and Incentive

Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science

Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany

Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany

12500

More information about this subseries at http://www.springer.com/series/1244

Qiang Yang Lixin Fan Han Yu (Eds.) •

•

Federated Learning Privacy and Incentive

123

Editors Qiang Yang WeBank Shenzhen, China

Lixin Fan WeBank Shenzhen, China

Hong Kong University of Science and Technology Hong Kong, Hong Kong Han Yu Nanyang Technological University Singapore, Singapore

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-63075-1 ISBN 978-3-030-63076-8 (eBook) https://doi.org/10.1007/978-3-030-63076-8 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Machine learning (ML) has shown significant potential for revolutionizing many important applications such as fraud detection in finance, medical diagnosis in healthcare, or speech recognition in automatic customer service. The traditional approach of training ML models requires large-scale datasets. However, with rising public concerns for data privacy protection, such an approach is facing tremendous challenges. Trust establishment techniques such as blockchains can help users ascertain the origin of the data and audit their usage. Nevertheless, we sti

Recommend Documents

Threats to Federated Learning

232 16 27MB

Deep Learning Pipeline Building a Deep Learning Model with TensorFlo

231 58 12MB

Federated Generative Adversarial Learning

207 66 65MB

Model Poisoning Defense on Federated Learning: A Validation Based Approach

179 3 27MB

Inverse Distance Aggregation for Federated Learning with Non-IID Data

220 73 27MB

Building Your First Deep Learning Model

195 3 15MB

Federated Learning for Healthcare Informatics

222 114 936KB

Collaborative Fairness in Federated Learning

219 94 27MB

Federated Learning Privacy and Incentive

252 83 27MB

Adaptive privacy-preserving federated learning

253 6 1MB

Federated Learning for Open Banking

466 119 449KB

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

170 63 718KB