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1st FL@IJCAI 2022: Vienna, Austria
- Randy Goebel
, Han Yu
Trustworthy Federated Learning - First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers. Lecture Notes in Computer Science 13448, Springer 2023, ISBN 978-3-031-28995-8 - Martin Isaksson
Adaptive Expert Models for Federated Learning. 1-16 - Zijian Li
Federated Learning with GAN-Based Data Synthesis for Non-IID Clients. 17-32 - Liu Yang
Practical and Secure Federated Recommendation with Personalized Mask. 33-45 - Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
A General Theory for Client Sampling in Federated Learning. 46-58 - Edvin Listo Zec, Ebba Ekblom, Martin Willbo, Olof Mogren, Sarunas Girdzijauskas:
Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration. 59-71 - Georgios Kollias, Theodoros Salonidis, Shiqiang Wang:
Sketch to Skip and Select: Communication Efficient Federated Learning Using Locality Sensitive Hashing. 72-83 - Giacomo Verardo
Fast Server Learning Rate Tuning for Coded Federated Dropout. 84-99 - Haley Hoech
FedAUXfdp: Differentially Private One-Shot Federated Distillation. 100-114 - Shuowei Cai
Secure Forward Aggregation for Vertical Federated Neural Networks. 115-129 - Shubao Zhao
Two-Phased Federated Learning with Clustering and Personalization for Natural Gas Load Forecasting. 130-143 - Jianping Cai
Privacy-Preserving Federated Cross-Domain Social Recommendation. 144-158
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