{"type":"ScholarlyArticle","title":"Communication-efficient and Scalable Decentralized Federated Edge Learning","authors":[{"name":"Austine Zong Han Yapp","slug":"austine-zong-han-yapp","given":"Austine Zong Han","family":"Yapp","affiliation":""},{"name":"Hong Soo Nicholas Koh","slug":"hong-soo-nicholas-koh","given":"Hong Soo Nicholas","family":"Koh","affiliation":""},{"name":"Yan Ting Lai","slug":"yan-ting-lai","given":"Yan Ting","family":"Lai","affiliation":""},{"name":"Jiawen Kang","slug":"jiawen-kang","given":"Jiawen","family":"Kang","affiliation":""},{"name":"Xuandi Li","slug":"xuandi-li","given":"Xuandi","family":"Li","affiliation":""},{"name":"Jer Shyuan Ng","slug":"jer-shyuan-ng","given":"Jer Shyuan","family":"Ng","affiliation":""},{"name":"Hongchao Jiang","slug":"hongchao-jiang","given":"Hongchao","family":"Jiang","affiliation":""},{"name":"Wei Yang Bryan Lim","slug":"wei-yang-bryan-lim","given":"Wei Yang Bryan","family":"Lim","affiliation":""},{"name":"Zehui Xiong","slug":"zehui-xiong","given":"Zehui","family":"Xiong","affiliation":""},{"name":"Dusit Niyato","slug":"dusit-niyato","given":"Dusit","family":"Niyato","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/720","url":"https://ijcai.org/proceedings/2021/720","pdf":"https://ijcai.org/proceedings/2021/0720.pdf","markdown":"https://ijcai.org/proceedings/2021/720.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/720","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Demo Track","track":"Demo Track","number":720,"pages":{"first":5032,"last":5035},"keywords":["Machine Learning: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}