24 citations · 49 across the 6 of their papers we have counts for
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cs.LG2025★ 1 cited
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
Xianke Qiang, Hongda Liu, Xinran Zhang +2
Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations a…
cs.LG2025
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions
Xianke Qiang, Zheng Chang, Ying-Chang Liang
Federated learning (FL) can fully leverage large-scale terminal data while ensuring privacy and security, and is considered as a distributed alternative for the centralized machine…
cs.LG2024★ 21 cited
Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing
Xianke Qiang, Zheng Chang, Yun Hu +2
Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge…