Communication-efficient and Scalable Decentralized Federated Edge Learning

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

doi:10.24963/ijcai.2021/720

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

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Demo Track. Pages 5032-5035.

https://doi.org/10.24963/ijcai.2021/720

Keywords

Machine Learning: General

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.

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, and Dusit Niyato. 2021. Communication-efficient and Scalable Decentralized Federated Edge Learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 5032–5035. https://doi.org/10.24963/ijcai.2021/720

@inproceedings{ijcai2021p720,
  title     = {Communication-efficient and Scalable Decentralized Federated Edge Learning},
  author    = {Yapp, Austine Zong Han and Koh, Hong Soo Nicholas and Lai, Yan Ting and Kang, Jiawen and Li, Xuandi and Ng, Jer Shyuan and Jiang, Hongchao and Lim, Wei Yang Bryan and Xiong, Zehui and Niyato, Dusit},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {5032--5035},
  year      = {2021},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2021/720},
  url       = {https://doi.org/10.24963/ijcai.2021/720},
}

TY  - CPAPER
TI  - Communication-efficient and Scalable Decentralized Federated Edge Learning
AU  - Yapp, Austine Zong Han
AU  - Koh, Hong Soo Nicholas
AU  - Lai, Yan Ting
AU  - Kang, Jiawen
AU  - Li, Xuandi
AU  - Ng, Jer Shyuan
AU  - Jiang, Hongchao
AU  - Lim, Wei Yang Bryan
AU  - Xiong, Zehui
AU  - Niyato, Dusit
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 5032
EP  - 5035
DO  - 10.24963/ijcai.2021/720
UR  - https://doi.org/10.24963/ijcai.2021/720
ER  -