Publications (7)
NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning
Zhen Fang, Miao Yang, Zehang Lin +6
The expanding scale of neural networks poses a major challenge for distributed machine learning, particularly under limited communication resources. While split learning (SL) allev…
Conflict-Aware Client Selection for Multi-Server Federated Learning
Mingwei Hong, Zheng Lin, Zehang Lin +7
Federated learning (FL) has emerged as a promising distributed machine learning (ML) that enables collaborative model training across clients without exposing raw data, thereby pre…
Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
Zhenguo Yang, Zehang Lin, Peipei Kang +3
In this paper, we propose to learn shared semantic space with correlation alignment () for multimodal data representations, which aligns nonlinear correlations of multim…
MMED: A Multi-domain and Multi-modality Event Dataset
Zhenguo Yang, Zehang Lin, Min Cheng +2
In this work, we construct and release a multi-domain and multi-modality event dataset (MMED), containing 25,165 textual news articles collected from hundreds of news media sites (…
SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
Zehang Lin, Miao Yang, Haihan Zhu +9
The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…
SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression
Zehang Lin, Zheng Lin, Miao Yang +7
The increasing complexity of neural networks poses a significant barrier to the deployment of distributed machine learning (ML) on resource-constrained devices, such as federated l…