23 citations · 31 across the 12 of their papers we have counts for
3 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…
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…