29 citations · 72 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
Yuanyuan Chen, Zichen Chen, Pengcheng Wu +1
Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose sign…
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
Yuanyuan Chen, Boyang Li, Han Yu +2
The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HYDRA, which in…
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Yang Liu, Anbu Huang, Yun Luo +7
Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to priv…
RPN: A Residual Pooling Network for Efficient Federated Learning
Anbu Huang, Yuanyuan Chen, Yang Liu +2
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. Due to m…