Trustworthy Federated Learning : 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.

Format:

Book

Conference/Event

Contributor:

Yu, Han (Assistant Professor), editor.

Goebel, Randy, editor.

Faltings, Boi, editor.

Fan, Lixin (Scientist), editor.

Xiong, Zehui, editor.

Conference Name:

International Workshop on Trustworthy Federated Learning (1st : 2022 : Vienna, Austria)

Series:

Lecture Notes in Artificial Intelligence, 2945-9141 ; 13448

Language:

English

Subjects (All):

Artificial intelligence.

Data protection.

Social sciences--Data processing.

Social sciences.

Application software.

Artificial Intelligence.

Data and Information Security.

Computer Application in Social and Behavioral Sciences.

Computer and Information Systems Applications.

Local Subjects:

Artificial Intelligence.

Data and Information Security.

Computer Application in Social and Behavioral Sciences.

Computer and Information Systems Applications.

Physical Description:

1 online resource (168 pages) : illustrations.

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

Summary:

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.

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.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Goebel, Randy Trustworthy Federated Learning

ISBN:

9783031289965

OCLC:

1374425264