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cs.LG2025
Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devices
Yuki Oda, Yuta Ono, Hiroshi Nakamura +1
The continuous scaling of deep neural networks has fundamentally transformed machine learning, with larger models demonstrating improved performance across diverse tasks. This grow…
cs.LG2025
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
Yuta Ono, Hiroshi Nakamura, Hideki Takase
Federated Active Learning (FAL) seeks to reduce the burden of annotation under the realistic constraints of federated learning by leveraging Active Learning (AL). As FAL settings m…