WirelessSenseLLM bridges unsegmented Wi-Fi CSI signals to LLMs via a CSI-to-Language Adapter for zero-shot human activity understanding and reasoning.
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FedMITR uses sparse model inversion and token relabeling to improve one-shot federated learning with ViTs under non-IID conditions, delivering a tighter generalization bound via algorithmic stability analysis and better empirical performance.
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WirelessSenseLLM: Zero-Shot Human Activity Understanding by Bridging Wireless Signals and Human Language
WirelessSenseLLM bridges unsegmented Wi-Fi CSI signals to LLMs via a CSI-to-Language Adapter for zero-shot human activity understanding and reasoning.
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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
FedMITR uses sparse model inversion and token relabeling to improve one-shot federated learning with ViTs under non-IID conditions, delivering a tighter generalization bound via algorithmic stability analysis and better empirical performance.