10 citations · 14 across the 6 of their papers we have counts for
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cs.LG2024★ 3 cited
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…
cs.NI2024★ 1 cited
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…
cs.LG2024★ 10 cited
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…