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  • Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
    In this paper, we propose a differentially private scheme for federated learning with adaptive noise (Adap DP-FL) Specifically, due to the gradient heterogeneity, we conduct adaptive gradient clipping for different clients and different rounds; due to the gradient convergence, we add decreasing noises accordingly
  • Differentially Private Federated Learning With an Adaptive Noise . . .
    DP-FL guarantees the privacy of FL at the cost of model performance degradation To balance the trade-off between model accuracy and security, we propose a differentially private federated learning scheme with an adaptive noise mechanism
  • Differentially Private Federated Learning With an Adaptive Noise Mechanism
    To avoid the potential privacy threat in FL, researchers propose the DP-FL strategy, which utilizes differential pri-vacy (DP) to add elaborate noise to the exchanged parameters to hide
  • ACLI-DPFL: Differentially Private Federated Learning with Adaptive . . .
    The meteoric rise of cross-silo Federated Learning (FL) is due to its ability to mitigate data breaches during collaborative training To further provide rigorous privacy protection with consideration of the varying privacy requirements across different
  • JeffffffFu Awesome-Differential-Privacy-and-Meachine-Learning
    提出了Harmony,用于包含数值和类别属性的多维数据的LDP下的均值和频数统计。 主要是连续型数据直接随机的对称扰动成两个相反数,然后保证均值无偏,误差边界比用Lap小。 核心是单维数据LDP的收集,多维是一个简单扩展。 单维下文章发现了拉普拉斯加噪和DM(Duchi)的优缺点,两个方法随着eps的增大有一个交点。 文章结合两个方法的优点提出PM方法,随后引入alpha参数升级为HM得到更小的误差边界 【vedio】 目前看起来较为新较为全面的LDP综述,对于LDP的各种机制和应用都有概括。 可以反复进行参考 针对LDP下的不同回答(属性),文章认为有些是敏感的需要保护,有些是不敏感的不需要保护,由此提出一种新的LDP叫ULDP,ULDP使得在同样的预算下做更少的扰动,使得方差误差更小。
  • Differentially Private Federated Learning with Adaptive Noise . . . - SSRN
    To address this problem, we propose a differential privacy federated learning framework with an adaptive noise mechanism and sharpness-aware minimization Specifically, during the local training phase on clients, we employ the Adaptive Sharpness-Aware Minimization (ASAM) optimizer to minimize the norm of local updates, generating locally flat
  • [PDF] Differential Privacy Federated Learning Based on Adaptive . . .
    An adaptive adjusted differential privacy federated learning method that flexibly adjusts the privacy budget within a given range based on the client’s data volume and training requirements, thereby alleviating the loss of privacy budget and the magnitude of model noise
  • Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
    To avoid the potential privacy threat in FL, researchers propose the DP-FL strategy, which utilizes differential privacy (DP) to add elaborate noise to the exchanged parameters to hide
  • Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
    We propose a differentially private scheme for fed-erated learning with an adaptive gradient clipping and an adaptive noise scale reduction We perform privacy loss analysis of our scheme using the notion of Rényi differential privacy (RDP) and prove that it satisfies the differential privacy
  • Adap DP-FL: Differentially Private Federated Learning with Adaptive . . .
    dblp: Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise For some weeks now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community





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