A Blockchain-based federated learning framework for secure aggregation and fair incentives

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-02-21 DOI:10.1080/09540091.2024.2316018

XiaoHui Yang, TianChang Li

引用次数: 0

Abstract

Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks under...

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基于区块链的联合学习框架，实现安全聚合和公平激励

作为一种包含隐私保护机制的机器学习框架，联合学习（FL）已经获得了突出的地位。然而，在...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.