JeffffffFu/Awesome-Differential-Privacy-and-Meachine-Learning
Differentially private federated learning: A systematic review (ACM Survey); Adap dp-fl: Differentially private federated learning with adaptive noise (TrustCom'2022)
This resource helps researchers and practitioners understand and implement differentially private federated learning (DP-FL). It synthesizes research papers, offers systematic reviews, and provides code references to ensure that machine learning models can be trained collaboratively across different datasets without compromising individual data privacy. Scientists, data privacy officers, and machine learning engineers working with sensitive data would find this useful.
383 stars. No commits in the last 6 months.
Use this if you need to build or evaluate machine learning systems that learn from distributed datasets while rigorously protecting the privacy of the individual data points involved.
Not ideal if you are looking for a plug-and-play solution for general machine learning tasks without a specific focus on differential privacy or federated learning.
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Sep 02, 2025
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