Trustworthy Federated Learning
First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
Published on 29. March 2023
Book
Paperback/Softback
X, 159 pages
978-3-031-28995-8 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2023
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
4 s/w Abbildungen, 49 farbige Abbildungen
X, 159 p. 53 illus., 49 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 10 mm
Weight
271 gr
ISBN-13
978-3-031-28995-8 (9783031289958)
DOI
10.1007/978-3-031-28996-5
Schweitzer Classification
Other editions
Additional editions
Randy Goebel | Han Yu | Boi Faltings
Trustworthy Federated Learning
First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
E-Book
03/2023
Springer
€58.84
Available for download
Content
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.