FedCal: Achieving Local and Global Calibration in Federated Learning via
Aggregated Parameterized Scaler

FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler

24 May 2024

Hongyi Peng

Papers citing "FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler"

9 / 9 papers shown

Title
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review Nikolas Koutsoubis Asim Waqas Yasin Yilmaz R. Ramachandran M. Schabath Ghulam Rasool 26 1 0 24 Sep 2024
Git Re-Basin: Merging Models modulo Permutation Symmetries Samuel K. Ainsworth J. Hayase S. Srinivasa MoMe 252 313 0 11 Sep 2022
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control Anastasios Nikolas Angelopoulos Stephen Bates Emmanuel J. Candès Michael I. Jordan Lihua Lei 97 125 0 03 Oct 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 840 0 01 Mar 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 180 355 0 07 Dec 2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates Lingkai Kong Jimeng Sun Chao Zhang UQCV 44 103 0 24 Aug 2020
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 212 542 0 30 Mar 2020
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 174 1,705 0 18 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016