FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

14 July 2023

Papers citing "FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout"

6 / 6 papers shown

Title
FNBench: Benchmarking Robust Federated Learning against Noisy Labels Xuefeng Jiang Jia Li Nannan Wu Z. F. Wu Xujing Li Sheng Sun Gang Xu Y. Wang Qi Li Min Liu FedML 47 3 0 10 May 2025
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction Xuefeng Jiang Sheng Sun Jia Li Jingjing Xue Runhan Li Zhiyuan Wu Gang Xu Yuwei Wang Min Liu FedML 30 10 0 08 Aug 2024
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices Dingzhu Wen Ki-Jun Jeon Kaibin Huang FedML 68 90 0 30 Sep 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 176 267 0 26 Feb 2021
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 162 760 0 28 Sep 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015