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…
FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs
Kahou Tam, Wei Niu, Yu Bao +3
Transformer-based models have enabled unprecedented capabilities across language, vision, and multimodal tasks. On-device fine-tuning of transformer models offers a privacy-preserv…
MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement
Yejia Liu, Hengle Jiang, Haoxian Liu +2
3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-robot collaboration, and immersive gaming, but real-world deployment rem…
MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding
Runxi Huang, Mingxuan Yu, Mingyu Tsoi +1
Real-time multimodal inference on resource-constrained edge devices is essential for applications such as autonomous driving, human-computer interaction, and mobile health. However…
Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT
Liyu Zhang, Yejia Liu, Kwun Ho Liu +2
A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes in sensor placement or ambient…