Choong Seon Hong

Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

2022 · arXiv (Cornell University)

Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a …

Swin Transformer-Based Dynamic Semantic Communication for Multi-User With Different Computing Capacity

2024 · IEEE Transactions on Vehicular Technology

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature …

Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training

2024 · arXiv (Cornell University)

Combining different data modalities enables deep neural networks to tackle complex tasks more effectively, making multimodal learning increasingly popular. To harness multimodal data closer to end users, it is essential to integrate multimodal learning with …

Additive Angular Margin Loss for Federated Learning in Image Classification

2025

Federated learning (FL) is a method that leverages data from multiple sources to enhance deep learning models while safeguarding data privacy. This approach finds applications in diverse domains such as healthcare, entertainment, and user experience …