计算机科学
异步通信
效率低下
声誉
联合学习
脆弱性(计算)
深度学习
计算机安全
可靠性(半导体)
人工智能
方案(数学)
机器学习
分布式计算
功率(物理)
计算机网络
物理
数学分析
社会学
量子力学
经济
微观经济学
社会科学
数学
作者
Zunming Chen,Hongyan Cui,Ensen Wu,Xi Yu
出处
期刊:Sensors
[MDPI AG]
日期:2022-01-17
卷期号:22 (2): 684-684
被引量:16
摘要
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.
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