3 papers
cs.NI2025
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…
cs.NI2025
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…
cs.LG2024
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…