Demo: Addressing Inter-Intra Patient Variability via Personalized Meta-Federated Learning in IoT-Enabled Health Monitoring
Author:
Affiliation:
1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3580252.3589415
Reference6 articles.
1. Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018. Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876 (2018).
2. Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning: A meta-learning approach. arXiv:2002.07948 (2020).
3. Personalized Neural Network for Patient-Specific Health Monitoring in IoT: A Metalearning Approach
4. Zhenge Jia Zhepeng Wang Feng Hong Lichuan Ping Yiyu Shi and Jingtong Hu. 2021. Learning to learn personalized neural network for ventricular arrhythmias detection on intracardiac EGMs. In IJCAI-21. 2606--2613.
5. Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan. 2019. Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488 (2019).
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