An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawitz et. al)" in Python.
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Aug 4, 2019 - Python
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secure-aggregation
Here are 14 public repositories matching this topic...
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May 15, 2023 - Rust
Privacy-Preserving Data Analysis using Pandas
pandas data-analytics data-analysis differential-privacy privacy-preserving-analytics secure-aggregation federated-analytics
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Oct 16, 2021 - Python
Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
python c cpp data-privacy commitment privacy-preserving federated-learning elliptic-curve secure-aggregation
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Aug 21, 2025 - C
Implementation of the Heflp, a framework enabling practical and overflow-safe federated learning.
homomorphic-encryption federated-learning privacy-preserving-machine-learning secure-aggregation risk-of-overflowing
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Sep 23, 2025 - Python
Privacy-first decentralized AI training network combining federated learning, blockchain incentives, and quantum-safe cryptography. Enable secure collaborative model development without sharing raw data.
python rust cryptography algorithms ipfs smart-contracts pytorch post-quantum-cryptography homomorphic-encryption federated-learning ipfs-blockchain zero-knowledge-proofs secure-aggregation decentralized-ai blockchain-ml privacy-preserving-ai
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Oct 9, 2025 - Python
Secure Aggregation with Shamir’s Method
shamir shamir-secret-sharing secure-multi-party-computation secure-aggregation secure-multiparty-computation
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Oct 20, 2021 - Python
An implementation of the secure aggregation algorithm for federated learning
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Nov 10, 2022 - Python
Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy
machine-learning privacy clustering kmeans differential-privacy federated-learning secure-aggregation
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Jun 14, 2025 - Python
A sublinear secure aggregation protocol implementation
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Jul 3, 2021 - Python
Comparison of several approaches for the PRIvate ESTimation of KL-Divergence (PRIEST-KLD)
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Nov 28, 2024 - Python
This repository explores federated deep generative models with PyTorch, featuring Conditional DCGAN, FedGAN v2, and custom synchronization strategies. It demonstrates client-server training with FedAvg, non-IID data splits, and GAN evaluation, providing a foundation for research in privacy-preserving generative modeling.
transformers pytorch differential-privacy edge-computing distributed-training federated-learning deep-generative-models federated-optimization privacy-preserving-ml diffusion-models secure-aggregation federated-gan non-iid-data federated-vae ml-reproducibility
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Oct 14, 2025 - Jupyter Notebook
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
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Oct 6, 2025 - Python
Experiments at the intersection of ML security & privacy: adversarial attacks/defenses (FGSM/PGD, adversarial training), differential privacy (DP-SGD, ε–δ), federated learning privacy (secure aggregation), and auditing (membership/model inversion). PyTorch notebooks + eval scripts.
security machine-learning privacy deep-learning pytorch differential-privacy robustness model-inversion adversarial-attacks federated-learning secure-aggregation dp-sgd membership-inference
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Oct 15, 2025 - Jupyter Notebook
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