8 citations · 12 across the 5 of their papers we have counts for
5 papers
Multimodal Federated Learning under Dual-Axis Modality Missingness
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5
Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modal…
(FL): Overcoming Few Labels in Federated Semi-Supervised Learning
Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin +1
Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches…
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +1
Many healthcare sensing applications utilize multimodal time-series data from sensors embedded in mobile and wearable devices. Federated Learning (FL), with its privacy-preserving…
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Jaemin Shin, Hyungjun Yoon, Seungjoo Lee +4
Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features s…
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Jaemin Shin, Yuanchun Li, Yunxin Liu +1
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data. Unlike centralized training that is usually based on carefully-orga…