AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks

no code implementations • 19 Mar 2024 • Zheng Lin, Guanqiao Qu, Wei Wei, Xianhao Chen, Kin K. Leung

In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation.

Edge-computing Federated Learning

Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

no code implementations • 28 Sep 2023 • Zheng Lin, Guanqiao Qu, Qiyuan Chen, Xianhao Chen, Zhe Chen, Kaibin Huang

In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs.

Edge-computing Quantization

Optimal Resource Allocation for U-Shaped Parallel Split Learning

no code implementations • 17 Aug 2023 • Song Lyu, Zheng Lin, Guanqiao Qu, Xianhao Chen, Xiaoxia Huang, Pan Li

In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks.

Split Learning in 6G Edge Networks

no code implementations • 21 Jun 2023 • Zheng Lin, Guanqiao Qu, Xianhao Chen, Kaibin Huang

With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence.

Edge-computing Federated Learning +1