Yuwei Wang
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The paper proposes PCDM, a diffusion-based framework that enables highly stealthy and effective data poisoning attacks against Federated Learning systems, significantly degrading global performance while evading detection.
The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation models for channel prediction against noise and interference.
This paper proposes ConsisFormer, a compute-efficient Transformer design for wireless foundation models (WFMs) using short-term channel consistency and adaptive token aggregation.
Papers
ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency
Yuwei Wang, Li Sun, Tingting Yang, Liwen Jing +3 more
This paper proposes ConsisFormer, a compute-efficient Transformer design for wireless foundation models (WFMs) using short-term channel consistency and adaptive token aggregation.