An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawitz et. al)" in Python.
-
Updated
Aug 4, 2019 - Python
An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawitz et. al)" in Python.
Privacy-Preserving Data Analysis using Pandas
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
Secure Aggregation with Shamir’s Method
An implementation of the secure aggregation algorithm for federated learning
Implementation of the Heflp, a framework enabling practical and overflow-safe federated learning.
A sublinear secure aggregation protocol implementation
Comparison of several approaches for the PRIvate ESTimation of KL-Divergence (PRIEST-KLD)
Privacy-first decentralized AI training network combining federated learning, blockchain incentives, and quantum-safe cryptography. Enable secure collaborative model development without sharing raw data.
Implementation of the Privacy Preserving Machine Learning with Homomorphic Encryption Described in Deliverable D3.1 of project Harpocrates, available at https://zenodo.org/records/15298272
Add a description, image, and links to the secure-aggregation topic page so that developers can more easily learn about it.
To associate your repository with the secure-aggregation topic, visit your repo's landing page and select "manage topics."