21 citations · 33 across the 5 of their papers we have counts for
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cs.LG2023
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…
cs.LG2023★ 1 cited
Fairness-Aware Client Selection for Federated Learning
Yuxin Shi, Zelei Liu, Zhuan Shi +1
Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server ca…
cs.LG2023★ 2 cited
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…