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ORCID1st FL@IJCAI 2022: Vienna, Austria
- Randy Goebel
, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong:
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, Edvin Listo Zec, Rickard Cöster, Daniel Gillblad, Sarunas Girdzijauskas:
Adaptive Expert Models for Federated Learning. 1-16 - Zijian Li, Jiawei Shao, Yuyi Mao, Jessie Hui Wang, Jun Zhang:
Federated Learning with GAN-Based Data Synthesis for Non-IID Clients. 17-32 - Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, Qiang 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, Daniel Barreira, Marco Chiesa, Dejan Kostic, Gerald Q. Maguire Jr.:
Fast Server Learning Rate Tuning for Coded Federated Dropout. 84-99 - Haley Hoech, Roman Rischke, Karsten Müller, Wojciech Samek:
FedAUXfdp: Differentially Private One-Shot Federated Distillation. 100-114 - Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang, Kun Guo, Kai Chen:
Secure Forward Aggregation for Vertical Federated Neural Networks. 115-129 - Shubao Zhao, Jia Liu, Guoliang Ma, Jie Yang, Di Liu, Zengxiang Li:
Two-Phased Federated Learning with Clustering and Personalization for Natural Gas Load Forecasting. 130-143 - Jianping Cai, Yang Liu, Ximeng Liu, Jiayin Li, Hongbin Zhuang:
Privacy-Preserving Federated Cross-Domain Social Recommendation. 144-158
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