Robust federated learning with noisy labels

S Yang, H Park, J Byun, C Kim - IEEE Intelligent Systems, 2022 - ieeexplore.ieee.org
Federated learning enables local devices to jointly train the server model while keeping the
data decentralized and private. In federated learning, all local data should be annotated by …

Differentially private cutmix for split learning with vision transformer

S Oh, J Park, S Baek, H Nam, P Vepakomma… - arXiv preprint arXiv …, 2022 - arxiv.org
Recently, vision transformer (ViT) has started to outpace the conventional CNN in computer
vision tasks. Considering privacy-preserving distributed learning with ViT, federated learning …

Fedaux: An efficient framework for hybrid federated learning

H Gu, B Guo, J Wang, W Sun, J Liu… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
As an enabler of sixth-generation communication technology (6G), Federated Learning (FL)
triggers a paradigm shift from" connected things" to" connected intelligence". FL implements …

StatMix: Data Augmentation Method that Relies on Image Statistics in Federated Learning

D Lewy, J Mańdziuk, M Ganzha… - … Conference on Neural …, 2022 - Springer
Availability of large amount of annotated data is one of the pillars of deep learning success.
Although numerous big datasets have been made available for research, this is often not the …

Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data

S Duan, C Liu, P Han, T He, Y Xu, Q Deng - arXiv preprint arXiv …, 2022 - arxiv.org
Non-independent and identically distributed (non-IID) data is a key challenge in federated
learning (FL), which usually hampers the optimization convergence and the performance of …

HPFL: Federated Learning by Fusing Multiple Sensor Modalities with Heterogeneous Privacy Sensitivity Levels

Y Chen, CF Hsu, CC Tsai, CH Hsu - … of the 1st International Workshop on …, 2022 - dl.acm.org
Solving classification problems to understand multi-modality sensor data has become
popular, but rich-media sensors, eg, RGB cameras and microphones, are privacy-invasive …