1 option
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
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 13448
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 13448
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Data protection.
- Social sciences-Data processing.
- 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 (X, 159 pages) : 53 illustrations, 49 illustrations in color.
- Edition:
- 1st ed. 2023.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2023.
- System Details:
- text file PDF
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-031-28996-5
- 9783031289965
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.