1 citations · 1 across the 7 of their papers we have counts for
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FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift
Huy Q. Le, Loc X. Nguyen, Yu Qiao +3
Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensitive applications. However, in…
Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G
Loc X. Nguyen, Ji Su Yoon, Huy Q. Le +6
The shift toward user-customized on-device learning places new demands on wireless systems: models must be trained on diverse, distributed data while meeting strict latency, bandwi…
High-Frequency First: A Two-Stage Approach for Improving Image INR
Sumit Kumar Dam, Mrityunjoy Gain, Eui-Nam Huh +1
Implicit Neural Representations (INRs) have emerged as a powerful alternative to traditional pixel-based formats by modeling images as continuous functions over spatial coordinates…
Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence
Yu Qiao, Apurba Adhikary, Huy Q. Le +3
Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL models remain susceptible to ad…
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining
Mrityunjoy Gain, Kitae Kim, Avi Deb Raha +4
In this paper, we propose the FedFeat+ framework, which distinctively separates feature extraction from classification. We develop a two-tiered model training process: following lo…
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance
Chu Myaet Thwal, Ye Lin Tun, Minh N. H. Nguyen +2
Beyond the success of Contrastive Language-Image Pre-training (CLIP), recent trends mark a shift toward exploring the applicability of lightweight vision-language models for resour…