3 papers
cs.MM2026
Omni-C: Compressing Heterogeneous Modalities into a Single Dense Encoder
Kin Wai Lau, Yasar Abbas Ur Rehman, Lai-Man Po +1
Recent multimodal systems often rely on separate expert modality encoders which cause linearly scaling complexity and computational overhead with added modalities. While unified Om…
cs.LG2025
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
Byoungjun Park, Pedro Porto Buarque de Gusmão, Dongjin Ji +1
Federated learning is a promising paradigm that utilizes distributed client resources while preserving data privacy. Most existing FL approaches assume clients possess labeled data…
cs.LG2024
FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning
Kin Wai Lau, Yasar Abbas Ur Rehman, Pedro Porto Buarque de Gusmão +3
Federated Learning (FL) has emerged as a privacy-preserving method for training machine learning models in a distributed manner on edge devices. However, on-device models face inhe…