9 citations · 14 across the 8 of their papers we have counts for
7 papers · 1 filter
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting
Xinxing Zhou, Jiaqi Ye, Shubao Zhao +6
In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional foreca…
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting
Shubao Zhao, Ming Jin, Zhaoxiang Hou +4
Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained si…
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…
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Has Sun, Xiaoli Tang, Chengyi Yang +5
Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data p…
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…
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
Lianlian Jiang, Yuexuan Wang, Wenyi Zheng +3
Federated learning (FL) and split learning (SL) are the two popular distributed machine learning (ML) approaches that provide some data privacy protection mechanisms. In the time-s…