Blockchained On-Device Federated Learning

被引：478

作者：

Kim, Hyesung ^{[1
]}

Park, Jihong ^{[2
]}

Bennis, Mehdi ^{[2
]}

Kim, Seong-Lyun ^{[1
]}

机构：

[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul 03722, South Korea

[2] Univ Oulu, Ctr Wireless Commun, Oulu 4500, Finland

来源：

IEEE COMMUNICATIONS LETTERS | 2020年 / 24卷 / 06期

关键词：

Computational modeling; Blockchain; Training; Servers; Nickel; Delays; Data models; On-device machine learning; federated learning; blockchain; latency;

D O I：

10.1109/LCOMM.2019.2921755

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.

引用

页码：1279 / 1283

页数：5

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