HCFL: A High Compression Approach for Communication-Efficient Federated
Learning in Very Large Scale IoT Networks

HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks

14 April 2022

Minh-Duong Nguyen

Diep N. Nguyen

Papers citing "HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks"

3 / 3 papers shown

Title
pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving Wei-Bin Kou Qingfeng Lin Ming Tang Sheng Xu Rongguang Ye ... Shuai Wang Guofa Li Zhenyu Chen Guangxu Zhu Yik-Chung Wu FedML 52 11 0 07 May 2024
Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning Do-Yup Kim Dami Lee Ji-Wan Kim Hyun-Suk Lee 25 4 0 02 Jul 2023
Label driven Knowledge Distillation for Federated Learning with non-IID Data Minh-Duong Nguyen Viet Quoc Pham D. Hoang Long Tran-Thanh Diep N. Nguyen W. Hwang 16 2 0 29 Sep 2022