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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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
FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning
Research testbed for federated learning with hybrid quantum-classical models and post-quantum protection (ML-KEM, ML-DSA, secure aggregation). Reproducible results: a 4-qubit VQC head vs a matched classical head, robust aggregation vs poisoning, PQC cost per round. PennyLane, PyTorch, Flower, FastAPI.
Implementation of the Heflp, a framework enabling practical and overflow-safe federated learning.
Privacy-first decentralized AI training network combining federated learning, blockchain incentives, and quantum-safe cryptography. Enable secure collaborative model development without sharing raw data.
Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy
An implementation of the secure aggregation algorithm for federated learning
PyFE4AI is a research-oriented Python library for functional encryption in trustworthy AI systems.
Secure Aggregation with Shamir’s Method
A sublinear secure aggregation protocol implementation
Federated Learning simulation using Flower with decentralized client training, secure aggregation concepts, and SHA-256 audit logging.
Privacy-preserving synthetic data for federated learning: IoT/MQTT gateways build a synthetic dataset under differential privacy and secure aggregation (Flower SecAgg+) to train an intrusion-detection system. FedDP-Marginal vs federated CVAE.
CKKS-based privacy-preserving fusion of multi-vendor object detections for autonomous vehicles.
Federated learning + secure aggregation + zero-knowledge norm-bound defence
Federated unlearning through functional encryption.
From-scratch federated learning: FedAvg / FedProx / SCAFFOLD, DP-SGD & secure aggregation, plus FedPer / Byzantine-robust / FedAdam / FedLoRA. 50/50 tests + CI, literature-cross-validated, with honest negative results on Non-IID MNIST.
Privacy-preserving federated learning in NumPy: FedAvg (IID/non-IID), Poisson DP-SGD with a subsampled-Gaussian RDP accountant, secure aggregation, and membership-inference attacks measuring real leakage vs epsilon.
Privacy-Preserving Cross-Organization Intelligence Federated learning + secure aggregation + MPC + differential privacy, governed and auditable — without raw data ever leaving its organization.
Functional encryption for federated learning with weight clustering and probabilistic filters.
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