Federated Learning with Differential Privacy:Algorithms and Performance Analysis 2024/2/11 大四做毕设的时候第一次读这篇论文,当时只读了前一部分,后面关于收敛界推导证明的部分没有看,现在重新完整阅读一下这篇文章。 本文贡献 提出了一种基于差分隐私 (DP) 概念的新框架,其中在聚合之前将人工噪声添加到客户端的...
Differential privacyDeep learning privacy-preservingFederated learningFast fourier transformPrivacy loss distributionSpurred by the simultaneous need for data privacy protection and data sharing, federated learning (FL) has been proposed. However, it still poses a risk of privacy leakage in it. This ...
To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of repre
The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly availabl
Personalized Federated Learning With Differential Privacy and Convergence Guarantee Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is capable of generating personalized models for heterogenous clients. ... K Wei,J Li,C Ma,... - 《IEEE Transactions on Information...
「联邦学习」（Federated Learning）就是为解决传统机器学习方法所面临的数据困境的一种新的尝试。这是一...
5.Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning 6.Federated Learning with Differential Privacy:Algorithms and Performance Analysis 7.Advances and Open Problems in Federated Learning 8.科研心得 9.联邦学习中的投毒攻击 ...
In this section, we give some basic notions and definitions of federated learning, then introduce the gradient descent algorithm in the machine learning optimization process. In addition, this section also explains the differential privacy method as the privacy security enhancement method in the process...
5.Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning 6.Federated Learning with Differential Privacy:Algorithms and Performance Analysis 7.Advances and Open Problems in Federated Learning 8.科研心得 9.联邦学习中的投毒攻击 ...
5.3. Federated learning with differential privacy In recent years, numerous works have concentrated on DP for FL. Wei et al. [34] introduce a novel DP-based framework incorporating artificial noise added to parameters at clients’ side before aggregation. The theoretical bound exposes a trade-off...