Federated learning (FL) enables decentralized model training without sharing raw data but remains vulnerable to adversarial attacks and often suffers from performance degradation under non-IID data distributions. By analyzing vanilla knowledge distillation (VKD) and adversarial knowledge distillation (AKD), we observe...
Yu Qiao, Apurba Adhikary, Huy Q. Le et al.· IEEE Journal on Selected Are...· 0 citations
A robust codec is proposed that effectively works under a diversity of compression rates and channel noise without re-training, while the greedy sub-carrier allocation lowers the communication time.
Loc X. Nguyen, Y. Park, Avi Deb Raha et al.· 0 citations
Humanoid robots are becoming an important part of embodied artificial intelligence, driven by advances in reinforcement learning for locomotion, world models for prediction, and vision-language-action models for general control. However, most of these systems remain static after deployment. A policy is trained offline...
Loc X. Nguyen, Avi Deb Raha, Huy Q. Le et al.· 1 citation
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