This paper introduces an innovative method for fine-tuning a larger multi-label model for
abnormality detection, utilizing a smaller trainer and advanced knowledge distillation …

Inference on edge devices (eg, smartphones) is becoming increasingly common, benefiting
from low-latency inference and user privacy by keeping data on-device. However, on-device …

Deep learning models have been deployed to a wide range of edge devices. Since the data
distribution on edge devices may differ from the cloud where the model was trained, it is …

On-device training is essential for user personalisation and privacy. With the pervasiveness
of IoT devices and microcontroller units (MCU), this task becomes more challenging due to …

With the continued advancement and widespread adoption of machine learning (ML)
models across various domains, ensuring user privacy and data security has become a …

Federated learning (FL) has become a prevalent distributed machine learning paradigm
with improved privacy. After learning, the resulting federated model should be further …

Major cloud service providers with well-equipped infrastructure, experienced machine
learning (ML) expertise, and enriched training datasets are building ML-as-a-Service …

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely
used in today's applications and services. However, with social awareness of privacy and …

Data is the key factor to drive the development of machine learning (ML) during the past
decade. However, high-quality data, in particular labeled data, is often hard and expensive …

Transfer learning is important for foundation models to adapt to downstream tasks. However,
many foundation models are proprietary, so users must share their data with model owners …