1 citations · 1 across the 4 of their papers we have counts for
4 papers
AirMoE: Realizing Over-the-Air Distributed Mixture-of-Experts Inference at the Wireless Edge
Huiling Yang, Zhanwei Wang, Kaibin Huang
Mixture-of-experts (MoE) architectures enable efficient large language model (LLM) inference at the wireless edge through sparse activation. The wireless distributed MoE (WIDE) arc…
SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks
Zhanwei Wang, Huiling Yang, Min Sheng +2
Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-intensive large language models (LL…
AirBreath Sensing: Protecting Over-the-Air Distributed Sensing Against Interference
Zhanwei Wang, Mingyao Cui, Huiling Yang +3
A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the phy…
Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity
Huiling Yang, Zhanwei Wang, Kaibin Huang
Federated learning (FL) has emerged as a popular approach for collaborative machine learning in sixth-generation (6G) networks, primarily due to its privacy-preserving capabilities…