2 citations · 4 across the 5 of their papers we have counts for
5 papers
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
Jixuan Cui, Jun Li, Zhen Mei +3
The challenge of data scarcity hinders the application of deep learning in industrial surface defect classification (SDC), as it's difficult to collect and centralize sufficient tr…
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Zhengyang Zhao, Haitao Yuan, Nan Jiang +3
Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks. The traffic of a road network can be affected…
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Zengxiang Li, Zhaoxiang Hou, Hui Liu +8
Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal larg…
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Yulan Gao, Zhaoxiang Hou, Chengyi Yang +2
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental ch…
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Yuanyuan Chen, Zichen Chen, Sheng Guo +6
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often i…