Back to https://github.com/Kwangkee/FL
[MAML-based FL Personalization]
-
Improving federated learning personalization via model agnostic meta learning, https://scholar.google.com/scholar?hl=ko&as_sdt=0%2C5&q=Improving+federated+learn-ing+personalization+via+model+agnostic+meta+learning&btnG=
-
Personalized federated learning: A meta-learning approach, https://scholar.google.com/scholar?hl=ko&as_sdt=0%2C5&q=Personalized+federated+learning%3A+A+meta-learning+approach&btnG=
-
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach, https://scholar.google.com/scholar?hl=ko&as_sdt=0%2C5&q=Personalized+feder-ated+learning+with+theoretical+guarantees%3A+A+model-agnostic+meta-learning+approach&btnG=
-
[DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction], https://ieeexplore.ieee.org/abstract/document/10026448
-
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning, https://scholar.google.com/citations?view_op=view_citation&hl=ko&user=WQyULhIAAAAJ&sortby=pubdate&citation_for_view=WQyULhIAAAAJ:lgwcVrK6X84C
FedABC: Targeting Fair Competition in Personalized Federated Learning, https://arxiv.org/abs/2302.07450
DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction, https://scholar.google.co.kr/scholar?hl=ko&as_sdt=0%2C5&q=DFML%3A+Dynamic+Federated+Meta-Learning+for+Rare+Disease+Prediction&btnG=
- Inspired by the framework (original federated meta-learning) in [12] described above, we utilize DFML to pre-dict rare diseases and apply two additional techniques to bolster its performance. We propose a novel dynamic weight-based fusion strategy and an inaccuracy-focused mechanism based on federated meta-learning for rare dis-ease prediction.
- [12] F Chen, M Luo, Z Dong, et al, “Federated meta-learning with fast convergence and efficient communication.” arXiv preprint arXiv:1802.07876, 2018.
- Some researchers use federated meta-learn-ing for personalized research [31], [32], [33], unlike feder-ated learning which focuses on the common output of all users.
- [31] A Fallah, A Mokhtari, A Ozdaglar, “Personalized federated learning: A meta-learning approach.” arXiv preprint arXiv:2002.07948, 2020.
- [32] Jiang, Y., Konečný, J., Rush, K, et al. "Improving federated learn-ing personalization via model agnostic meta learning." arXiv pre-print arXiv:1909.12488 ,2019.
- [33] Fallah, A., Mokhtari, A. and Ozdaglar, A., “Personalized feder-ated learning with theoretical guarantees: A model-agnostic meta-learning approach.” Advances in Neural Information Pro-cessing Systems, 33, pp.3557-3568, 2020.
Back to the Top
Back to https://github.com/Kwangkee/FL