6 papers · 1 filter
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…
Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learning
Jialei Tan, Zheng Lin, Xiangming Cai +4
Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices. However, the transmission of intermediate featur…
GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
Zheng Lin, Ons Aouedi, Zihan Fang +4
The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices. Parallel split learning (P…
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…
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
Zheng Lin, Zhe Chen, Xianhao Chen +2
Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing…
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
Zheng Lin, Yuxin Zhang, Zhe Chen +6
Recently, large language models (LLMs) have achieved remarkable breakthroughs, revolutionizing the natural language processing domain and beyond. Due to immense parameter sizes, fi…