---
title: "Communication-efficient and Scalable Decentralized Federated Edge Learning"
authors:
  - "Austine Zong Han Yapp"
  - "Hong Soo Nicholas Koh"
  - "Yan Ting Lai"
  - "Jiawen Kang"
  - "Xuandi Li"
  - "Jer Shyuan Ng"
  - "Hongchao Jiang"
  - "Wei Yang Bryan Lim"
  - "Zehui Xiong"
  - "Dusit Niyato"
doi: "10.24963/ijcai.2021/720"
url: "https://ijcai.org/proceedings/2021/720"
pdf: "https://ijcai.org/proceedings/2021/0720.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "5032-5035"
track: "Demo Track"
keywords:
  - "Machine Learning: General"
---

# Communication-efficient and Scalable Decentralized Federated Edge Learning

Austine Zong Han Yapp, Hong Soo Nicholas Koh, Yan Ting Lai, Jiawen Kang, Xuandi Li, Jer Shyuan Ng, Hongchao Jiang, Wei Yang Bryan Lim, Zehui Xiong, Dusit Niyato

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 5032-5035. DOI: [10.24963/ijcai.2021/720](https://doi.org/10.24963/ijcai.2021/720)

## Abstract

Federated Edge Learning (FEL) is a distributed Machine Learning (ML) framework for collaborative training on edge devices. FEL improves data privacy over traditional centralized ML model training by keeping data on the devices and only sending local model updates to a central coordinator for aggregation. However, challenges still remain in existing FEL architectures where there is high communication overhead between edge devices and the coordinator. In this paper, we present a working prototype of blockchain-empowered and communication-efficient FEL framework, which enhances the security and scalability towards large-scale implementation of FEL.