Collaborative Fairness in Federated Learning
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus low utility. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate loca
- PDF / 28,472,924 Bytes
- 291 Pages / 439.37 x 666.142 pts Page_size
- 94 Downloads / 302 Views
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
