102 citations · 125 across the 9 of their papers we have counts for
5 papers · 1 filter
Label driven Knowledge Distillation for Federated Learning with non-IID Data
Minh-Duong Nguyen, Quoc-Viet Pham, Dinh Thai Hoang +3
In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an envi…
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate
Xuan-Tung Nguyen, Minh-Duong Nguyen, Quoc-Viet Pham +2
Recently, a considerable amount of works have been made to tackle the communication burden in federated learning (FL) (e.g., model quantization, data sparsification, and model comp…
Aerial Computing: A New Computing Paradigm, Applications, and Challenges
Quoc-Viet Pham, Rukhsana Ruby, Fang Fang +5
In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemente…
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks
Minh-Duong Nguyen, Sang-Min Lee, Quoc-Viet Pham +3
Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centra…
AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing
Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang +4
Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (…