5 papers · 1 filter
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Changyu Li, Shuanghong Huang, Jiashen Liu +5
Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data. However, in mobile deployments, the traini…
Reliability-Calibrated Edge-IoT Early Fault Warning for Rotating Machinery with a Physics-Guided Tiny-Mamba Transformer
Changyu Li, Huabei Nie, Xiaoya Ni +4
Industrial Internet of Things (IIoT) systems increasingly rely on distributed vibration sensing to support predictive maintenance of rotating machinery. In practical deployments, h…
A Fast and Flat Federated Learning Method via Weighted Momentum and Sharpness-Aware Minimization
Tianle Li, Yongzhi Huang, Linshan Jiang +5
In federated learning (FL), models must \emph{converge quickly} under tight communication budgets while \emph{generalizing} across non-IID client distributions. These twin requirem…
FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios
Tianle Li, Yongzhi Huang, Linshan Jiang +5
Federated Learning (FL) enables decentralized model training while preserving data privacy. Despite its benefits, FL faces challenges with non-identically distributed (non-IID) dat…
CODA: A Continuous Online Evolve Framework for Deploying HAR Sensing Systems
Minghui Qiu, Jun Chen, Lin Chen +4
In always-on HAR deployments, model accuracy erodes silently as domain shift accumulates over time. Addressing this challenge requires moving beyond one-off updates toward instance…