Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning

Author:

Xiong Baochen1ORCID,Yang Xiaoshan2ORCID,Song Yaguang2ORCID,Wang Yaowei3ORCID,Xu Changsheng2ORCID

Affiliation:

1. Institute of Automation, Chinese Academy of Sciences, Peng Cheng Laboratory, & University of Chinese Academy of Sciences, Beijing, China

2. Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, & Peng Cheng Laboratory, Beijing, China

3. Peng Cheng Laboratory, Shenzhen, China

Funder

Beijing Natural Science Foundation

National Natural Science Foundation of China

Publisher

ACM

Reference46 articles.

1. Durmus Alp Emre Acar , Yue Zhao , Ramon Matas Navarro , Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama. 2021 . Federated learning based on dynamic regularization. arXiv preprint arXiv:2111.04263. Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama. 2021. Federated learning based on dynamic regularization. arXiv preprint arXiv:2111.04263.

2. Gustavo Aguilar Viktor Rozgić and Weiran Wang. 2019. Multimodal and multi-view models for emotion recognition. arXiv preprint arXiv:1906.10198. Gustavo Aguilar Viktor Rozgić and Weiran Wang. 2019. Multimodal and multi-view models for emotion recognition. arXiv preprint arXiv:1906.10198.

3. Manoj Ghuhan Arivazhagan , Vinay Aggarwal , Aaditya Kumar Singh, and Sunav Choudhary . 2019 . Federated learning with personalization layers. arXiv preprint arXiv:1912.00818. Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818.

4. Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

5. Deep Adversarial Learning for Multi-Modality Missing Data Completion

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