Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning

1 February 2022

Papers citing "Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning"

9 / 9 papers shown

Title
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yigitsoy Daniel Rueckert Georgios Kaissis 37 0 0 01 Oct 2024
Learning to Project for Cross-Task Knowledge Distillation Dylan Auty Roy Miles Benedikt Kolbeinsson K. Mikolajczyk 40 0 0 21 Mar 2024
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts Jiayi Chen Benteng Ma Hengfei Cui Yong-quan Xia OOD FedML 29 12 0 05 Dec 2023
Federated Active Learning for Target Domain Generalisation Razvan Caramalau Binod Bhattarai Danail Stoyanov OOD FedML 32 1 0 04 Dec 2023
Re-thinking Federated Active Learning based on Inter-class Diversity Sangmook Kim Sangmin Bae Hwanjun Song Se-Young Yun FedML 30 14 0 22 Mar 2023
Knowledge-Aware Federated Active Learning with Non-IID Data Yu Cao Ye-ling Shi Baosheng Yu Jingya Wang Dacheng Tao FedML 21 17 0 24 Nov 2022
The Future of Consumer Edge-AI Computing Stefanos Laskaridis Stylianos I. Venieris Alexandros Kouris Rui Li Nicholas D. Lane 45 8 0 19 Oct 2022
Federated Learning with Uncertainty via Distilled Predictive Distributions Shreyansh P. Bhatt Aishwarya Gupta Piyush Rai FedML 21 10 0 15 Jun 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015