TSViT: A Time Series Vision Transformer for Fault Diagnosis

no code implementations • 12 Nov 2023 • Shouhua Zhang, Jiehan Zhou, Xue Ma, Chenglin Wen, Susanna Pirttikangas, Chen Yu, Weishan Zhang, Chunsheng Yang

Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) face limitations in capturing temporal features (i. e., the variation of vibration signals over time).

Time Series

Feature-context driven Federated Meta-Learning for Rare Disease Prediction

no code implementations • 29 Dec 2021 • Bingyang Chen, Tao Chen, Xingjie Zeng, Weishan Zhang, Qinghua Lu, Zhaoxiang Hou, Jiehan Zhou, Sumi Helal

Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.

Disease Prediction Federated Learning +1

A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things

no code implementations • 21 Apr 2021 • Jiehan Zhou, Shouhua Zhang, Qinghua Lu, Wenbin Dai, Min Chen, Xin Liu, Susanna Pirttikangas, Yang Shi, Weishan Zhang, Enrique Herrera-Viedma

Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.

Edge-computing Federated Learning +1