Work

Poster: Hierarchical Clustered Federated Learning Framework for IoT in Remote Areas

Year: 2024

Type: conference-paper

Abstract: Unmanned Aerial Vehicles (UAVs) provide an effective solution for supporting Federated Learning (FL) in remote areas lacking infrastructure, but the non-IID nature of data affects FL efficiency. We propose a hierarchical clustering method with pipelined execution for UAV-assisted FL, clustering IoT devices based on computational capacity and data similarity to ensure balanced participation and faster convergence. Simulation results demonstrate improved learning efficiency and stability in non-IID environments, validating the method's effectiveness.

Source: ACM Conference on Embedded Networked Sensor Systems (SenSys)

DOI: 10.1145/3666025.3699409

Authors: Chanmin Lee, Shinyoung Cho, Taeyoung Kim, Sukyoung Lee

Institution: Yonsei University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea