Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data

2 code implementations • 15 Jul 2022 • Seyoung Ahn, Soohyeong Kim, Yongseok Kwon, Joohan Park, Jiseung Youn, Sunghyun Cho

To address the aforementioned challenge, we propose a novel diffusion strategy of the machine learning (ML) model (FedDif) to maximize the FL performance with non-IID data.

Federated Learning Model Compression

Autonomous Power Allocation based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network

no code implementations • 8 Feb 2018 • Jeehyeong Kim, Joohan Park, Jaewon Noh, Sunghyun Cho

For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system, there is a limit to allocating resources considering a complicated interference between different links in a centralized manner.