近些年, 联邦学习(Federated learning, FL) 由于能够打破数据壁垒, 实现孤岛数据价值变现,
受到了工业界和学术界的广泛关注. 然而, 在实际工程应用中, 联邦学习存在着数据隐私泄露和 …

近年来, 联邦学习成为解决机器学习中数据孤岛与隐私泄露问题的新思路. 联邦学习架构不需要
多方共享数据资源, 只要参与方在本地数据上训练局部模型, 并周期性地将参数上传至服务器来 …

Abstract Machine learning has greatly improved the convenience of modern life. As the
deployment scale of machine learning grows larger, the corresponding data scale also …

Federated learning (FL) enables multiple worker devices share local models trained on their
private data to collaboratively train a machine learning model. However, local models are …

摘要联邦学习能够在保障本地数据隐私前提下利用分布式数据和计算资源实现机器学习模型
联合训练. 现有异步联邦学习有效解决了同步联邦学习所存在的计算资源浪费 …

为了解决联邦学习数据安全以及加密后通信开销大等问题, 提出了一种基于同态加密的高效安全
联邦聚合框架. 在联邦学习过程中, 用户数据的隐私安全问题亟须解决, 然而在训练过程中采用 …

In the area of privacy protection, federated learning has received a lot of attention as an
emerging distributed network training model that can effectively protect the privacy of users' …

针对联邦学习存在处理大多数不规则用户易引起聚合效率降低, 以及采用明文通信导致参数隐私
泄露的问题, 基于设计的安全除法协议构建针对不规则用户鲁棒的隐私保护联邦学习框架 …

The success of machine learning algorithms often relies on a large amount of high-quality
data to train well-performed models. However, data is a valuable resource and are always …

As a typical application of decentralization, federated learning prevents privacy leakage of
crowdsourcing data for various training tasks. Instead of transmitting actual data, federated …