4 papers · 1 filter
PSL-RGHA: Parallel Split Learning with Reference Gradient Half-Space Alignment for Mitigating Gradient Divergence
Han Li, Zheng Lin, Miao Yang +9
The escalating complexity of deep neural networks hinders the deployment of federated learning (FL) on resource-constrained devices, especially for multimedia applications such as…
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…
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…
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…