.png){kind=icon}
返回
A decentralized asynchronous federated learning framework for edge devices
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2024.107683.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The traditional synchronous federated learning framework ensures global model consistency and accuracy. However, it is limited by the computational power differences between devices and the influence of non-IID data, which leads to inefficient training and insufficient model generalization performance. In this paper, we propose a decentralized asynchronous federated learning framework. The framework uses smart contracts deployed on the blockchain to manage edge devices for enhanced flexibility. At first, the framework performs model aggregation and validation through the use of consensus groups. It eliminates the potential single point of failure associated with centralized parameter servers. In addition, we propose a Federated Learning with Dynamically Growing Cache (FedDgc) method in a non-IID environment. The method reduces redundant gradient information exchange during initial feature extraction while maintaining the learning capability of the global model. Finally, the experimental results show that our framework has better test performance and guarantees the convergence speed of the model during training.
Keyword:
Federated learning
Blockchain
Smart contract
Edge device
期刊
F
IF:
6.1
论文数:
6.8K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
ResNet and its application to medical image processing: Research progress and challengesResNet及其在医学图像处理中的应用: 研究进展与挑战
PREAI
分享收藏Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data在非IID数据上实现具有无界陈旧梯度的高效稳定的K异步联合学习
OAAI
OAAI分享收藏FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge ComputingFedSA: 异构边缘计算中的半异步联邦学习机制OAAI
OAAI分享收藏
查看更多内容