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Trustworthy Federated Learning [electronic resource] : First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers / edited by Randy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong.
Author
International Workshop on Trustworthy Federated Learning (1st : 2022 : Vienna, Austria)
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Format
Book
Language
English
Εdition
1st ed. 2023.
Published/Created
Cham : Springer International Publishing : Imprint: Springer, 2023.
Description
1 online resource (168 pages) : illustrations.
Details
Subject(s)
Artificial intelligence
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Data protection
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Social sciences
—
Data processing
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Application software
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Editor
Yu, Han (Assistant Professor)
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Goebel, Randy
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Faltings, Boi
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Fan, Lixin (Scientist)
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Xiong, Zehui
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Series
Lecture Notes in Artificial Intelligence, 13448
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Lecture Notes in Artificial Intelligence, 2945-9141 ; 13448
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Summary note
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. The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.
Bibliographic references
Includes bibliographical references and index.
Contents
Adaptive Expert Models for Personalization in Federated Learning
Federated Learning with GAN-based Data Synthesis for Non-iid Clients
Practical and Secure Federated Recommendation with Personalized Mask
A General Theory for Client Sampling in Federated Learning
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
Sketch to Skip and Select: Communication Efficient Federated Learning using Locality Sensitive Hashing
Fast Server Learning Rate Tuning for Coded Federated Dropout
FedAUXfdp: Differentially Private One-Shot Federated Distillation
Secure forward aggregation for vertical federated neural network
Two-phased Federated Learning with Clustering and Personalization for Natural Gas Load Forecasting
Privacy-Preserving Federated Cross-Domain Social Recommendation.
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ISBN
9783031289965 ((electronic bk.))
OCLC
1374425264
Doi
10.1007/978-3-031-28996-5
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Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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