.png){kind=icon}
返回
Blockchain-Enabled Asynchronous Federated Learning in Edge Computing
.png){kind=icon}
OA
AI
DOI：10.3390/s21103335.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.
Keyword:
federated learning
blockchain
edge computing
asynchronous convergence
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Federated learning of predictive models from federated Electronic Health Records从联邦电子健康记录中联合学习预测模型OAAI
OAAI分享收藏
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated LearningIn-Edge AI: 通过联合学习实现移动边缘计算、缓存和通信的智能化.png)
IEEE NETWORKOAAI
IEEE NETWORKIF6.3
OAAI分享收藏Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things边缘计算支持的物联网中基于联合学习的计算卸载优化IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing在雾计算中使用区块链联合学习的分散隐私
PREAI
分享收藏分享收藏Artificial Bee Colony Algorithm for Economic Load Dispatch Problem with Non-smooth Cost Functions非平稳成本函数经济负荷分配问题的人工蜂群算法
PREAI
分享收藏
查看更多内容