5 papers
Optimizing Split Federated Learning with Unstable Client Participation
Wei Wei, Zheng Lin, Xihui Liu +3
To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation…
Pipelining Split Learning in Multi-hop Edge Networks
Wei Wei, Zheng Lin, Tao Li +2
To support large-scale model training, split learning (SL) enables multiple edge devices/servers to share the intensive training workload. However, most existing works on SL focus…
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
Zheng Lin, Guanqiao Qu, Wei Wei +2
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated lear…
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
Zheng Lin, Wei Wei, Zhe Chen +4
As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated le…
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
Guanqiao Qu, Qiyuan Chen, Wei Wei +3
On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and priv…