4 papers
Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
Kangmin Kim, Jaeyoung Song
We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless channels without sharing local…
Separate Aggregation of Split Network for Personalized Federated Learning
Yunseok Kang, Jaeyoung Song
Federated learning enables collaborative model training without sharing raw data, but its performance can degrade substantially under heterogeneous client data distributions. A sin…
Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel
Jaeyoung Song, Jun-Pyo Hong
In this paper, we consider asynchronous federated learning (FL) over time-division multiple access (TDMA)-based communication networks. Considering TDMA for transmitting local upda…
Optimal Batch Allocation for Wireless Federated Learning
Jaeyoung Song, Sang-Woon Jeon
Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a…