Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

W Guo, Y Wang, X Chen, P Jiang - Journal of intelligent manufacturing, 2024 - Springer
Abstract Machine learning with considering data privacy-preservation and personalized
models has received attentions, especially in the manufacturing field. The data often exist in …

Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation

S Rajendran, W Pan, MR Sabuncu, Y Chen, J Zhou… - Patterns, 2024 - cell.com
In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …

Fedltn: Federated learning for sparse and personalized lottery ticket networks

V Mugunthan, E Lin, V Gokul, C Lau, L Kagal… - … on Computer Vision, 2022 - Springer
Federated learning (FL) enables clients to collaboratively train a model, while keeping their
local training data decentralized. However, high communication costs, data heterogeneity …

Covid-EnsembleNet: an ensemble based approach for detecting Covid-19 by utilising chest X-Ray images

A Al–Monsur, MDR Kabir, AM Ar–Rafi… - 2022 IEEE World AI …, 2022 - ieeexplore.ieee.org
Covid-19 is still running rampant around the globe. With the recent emergence of rapidly
spreading variants, the necessity for testing becomes ever more acute. In this study, firstly, a …

Learning critically: Selective self-distillation in federated learning on non-iid data

Y He, Y Chen, XD Yang, H Yu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Federated learning (FL) enables multiple clients to collaboratively train a global model while
keeping local data decentralized. Data heterogeneity (non-IID) across clients has imposed …

Privacy-preserving federated learning in healthcare

SH Moon, WH Lee - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Federated learning (FL) has received great attention in healthcare primarily due to its
decentralized, collaborative nature of building a machine learning (ML) model. Over the …

Personalized driving assistance algorithms: Case study of federated learning based forward collision warning

R Yu, R Zhang, H Ai, L Wang, Z Zou - Accident Analysis & Prevention, 2022 - Elsevier
Current designs of advanced driving assistance systems (ADAS) mainly developed uniform
collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead …

SemiPFL: Personalized semi-supervised federated learning framework for edge intelligence

A Tashakori, W Zhang, ZJ Wang… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Recent advances in wearable devices and Internet of Things (IoT) have led to massive
growth in sensor data generated in edge devices. Labeling such massive data for …

Federated Transfer Learning with Multimodal Data

Y Sun - arXiv preprint arXiv:2209.03137, 2022 - arxiv.org
Smart cars, smartphones and other devices in the Internet of Things (IoT), which usually
have more than one sensors, produce multimodal data. Federated Learning supports …

Federated Object Detection Scenarios for Intelligent Vehicles: Review, Case Studies, Experiments and Discussions

O Urmonov, S Sajid, Z Aziz… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The performance of intelligent vehicles (IVs) in object detection relies not only on the design
or scale of the CNN model they use but also on how effectively they share their acquired …