.png){kind=icon}
返回
HFedCWA: heterogeneous federated learning algorithm based on contribution-weighted aggregation
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-024-06123-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The aim of heterogeneous federated learning (HFL) is to address the issues of data heterogeneity, computational resource disparity, and model generalizability and security in federated learning (FL). To facilitate the collaborative training of data and enhance the predictive performance of models, a heterogeneous federated learning algorithm based on contribution-weighted aggregation (HFedCWA) is proposed in this paper. First, weights are assigned on the basis of the distribution differences and quantities of heterogeneous device data, and a contribution-based weighted aggregation method is introduced to dynamically adjust weights and balance data heterogeneity. Second, personalized strategies based on regularization are formulated for heterogeneous devices with different weights, enabling each device to participate in the overall task in an optimal manner. Differential privacy methods are concurrently utilized in FL training to further enhance the security of the system. Finally, experiments are conducted under various data heterogeneity scenarios using the MNIST and CIFAR10 datasets, and the results demonstrate that the HFedCWA can effectively improve the model's generalizability ability and adaptability to heterogeneous data, thereby enhancing the overall efficiency and performance of the HFL system.
Keyword:
Federated learning
Data heterogeneity
Aggregation algorithms
Personalized methods
Differential privacy
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
Interpretation of Radio-Echo Returns from Internal Water Bodies in Variegated Glacier, Alaska, U.S.A.美国阿拉斯加杂色冰川内部水体的无线电回波回波解释
OAAI
OAAI分享收藏
分享收藏
查看更多内容