Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review
FX Gaya-Morey, C Manresa-Yee, JM Buades-Rubio - Applied Intelligence, 2024 - Springer
As the proportion of elderly individuals in developed countries continues to rise globally,
addressing their healthcare needs, particularly in preserving their autonomy, is of paramount …
addressing their healthcare needs, particularly in preserving their autonomy, is of paramount …
Automated federated pipeline for parameter-efficient fine-tuning of large language models
Recently, there has been a surge in the development of advanced intelligent generative
content (AIGC), especially large language models (LLMs). However, for many downstream …
content (AIGC), especially large language models (LLMs). However, for many downstream …
Edgefm: Leveraging foundation model for open-set learning on the edge
Deep Learning (DL) models have been widely deployed on IoT devices with the help of
advancements in DL algorithms and chips. However, the limited resources of edge devices …
advancements in DL algorithms and chips. However, the limited resources of edge devices …
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease
Alzheimer's Disease (AD) and related dementia are a growing global health challenge due
to the aging population. In this paper, we present ADMarker, the first end-to-end system that …
to the aging population. In this paper, we present ADMarker, the first end-to-end system that …
Fedmm: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology
The fusion of complementary multimodal information is crucial in computational pathology
for accurate diagnostics. However, existing multimodal learning approaches necessitate …
for accurate diagnostics. However, existing multimodal learning approaches necessitate …
Hybrid Federated Learning for Multimodal IoT Systems
Multimodal Federated Learning (FL) targets the intersection of two promising research
directions in IoT scenarios: leveraging complementary multimodal information to enhance …
directions in IoT scenarios: leveraging complementary multimodal information to enhance …
A survey of multimodal federated learning: background, applications, and perspectives
Abstract Multimodal Federated Learning (MMFL) is a novel machine learning technique that
enhances the capabilities of traditional Federated Learning (FL) to support collaborative …
enhances the capabilities of traditional Federated Learning (FL) to support collaborative …
EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
The rise of mobile devices with abundant sensory data and local computing capabilities has
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …
ClassTer: Mobile Shift-Robust Personalized Federated Learning via Class-Wise Clustering
The rise of mobile devices with abundant sensor data and computing power has driven the
trend of federated learning (FL) on them. Personalized FL (PFL) aims to train tailored models …
trend of federated learning (FL) on them. Personalized FL (PFL) aims to train tailored models …
ERL-MR: Harnessing the Power of Euler Feature Representations for Balanced Multi-modal Learning
Multi-modal learning leverages data from diverse perceptual media to obtain enriched
representations, thereby empowering machine learning models to complete more complex …
representations, thereby empowering machine learning models to complete more complex …