A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

An Open Framework for Federated Learning.

Official code for "DCT-CryptoNets: Scaling Private Inference in the Frequency Domain" [ICLR 2025]

A Privacy-Preserving Federated Learnig benchamarking framework, based on TensorFlow/Keras and OpenFHE

A curated collection of privacy-preserving machine learning techniques, tools, and practical evaluations. Focuses on differential privacy, federated learning, secure computation, and synthetic data generation for implementing privacy in ML workflows.

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.

Sovereign Map is a production-grade, Byzantine-tolerant Federated Learning framework. Utilizing the Mohawk Protocol for streaming aggregation, it achieves a 224x memory reduction, enabling secure orchestration of 100M+ nodes via TPM 2.0 hardware-rooted trust. Features full-stack observability with Prometheus & Grafana, built-in tokenomics telemetry

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My journey from law to code: Projects in Privacy-Preserving ML, LegalTech automation, and regulatory compliance systems.

TZDC - A Python library for privacy-enhancing data operations using cryptographic fragmentation and temporal key expiration.

Docs: https://erasus.readthedocs.io/en/latest/ Forget data from any foundation model without retraining. Erasus surgically removes concepts, behaviors, or training samples from LLMs, VLMs, and Diffusion models using coreset selection. 90% less compute, certified removal, multimodal support.

A comprehensive evaluation of Machine Unlearning via Task Arithmetic on CNN architectures (ResNet-18, VGG-11, MobileNetV2). Includes SOTA metrics like ZRF, MIA, and the novel Anamnesis Index (AIN) for Rebound Effect analysis.

Proxy simulation for evaluating encrypted LLM accuracy without running full CKKS inference. IIT Big Data X REU 2025, eScience 2025.

Detection of rare child diseases by applying graph machine learning to a remote dataset with federated machine learning

A deep learning solution for brain tumor segmentation using multi-modal MRI scans, integrating U-Net models, differential privacy, adversarial training, and explainability (Grad-CAM, attention scores) for robust and trustworthy medical AI.

Adaptive Federated Learning Framework (AFLF): a distributed ML systems project implementing dynamic client selection, differential privacy, adaptive optimization, and communication-efficient federated training with reproducible experiments and a Streamlit research dashboard.

Privacy-preserving federated learning system — FedAvg across 10 distributed clients (99.2% MNIST accuracy), Gaussian differential privacy with RDP moments accountant, non-IID Dirichlet data partitioning, centralized vs. federated accuracy comparison, live FastAPI dashboard with real-time convergence plots — PyTorch · no raw data sharing

Privacy-preserving federated learning framework for staffing-acuity mismatch prediction in Long-Term Care using CTGAN synthetic data, differential privacy, and XAI auditing.

The uv of federated learning — Rust core for aggregation and attack sim, Python API for HuggingFace, PEFT, and PyTorch.

A privacy-preserving machine learning solution using Federated Learning (FedAvg) to predict Diabetes Mellitus across simulated hospitals without compromising sensitive patient data.