TY  - BOOK
AU  - Goebel,Randy
AU  - Yu,Han
AU  - Faltings,Boi
AU  - Fan,Lixin
AU  - Xiong,Zehui
ED  - SpringerLink (Online service)
TI  - Trustworthy Federated Learning: First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers 
T2  - Lecture Notes in Artificial Intelligence,
SN  - 9783031289965
AV  - Q334-342 
U1  - 006.3 23
PY  - 2023///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Artificial intelligence
KW  - Data protection
KW  - Social sciences
KW  - Data processing
KW  - Application software
KW  - Artificial Intelligence
KW  - Data and Information Security
KW  - Computer Application in Social and Behavioral Sciences
KW  - Computer and Information Systems Applications
N1  - Acceso multiusuario; 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-28996-5
ER  -