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Privacy-Preserving & Federated Data Science

Federated learning and secure computation frameworks like TensorFlow Federated, PySyft, and OpenMined let data scientists train models and run analyses on decentralized private data without it ever leaving the owner's s…

14 projects

See methodology for ranking rules; order uses public GitHub metrics within this scenario.

1–14 of 14

Rank Project Stars Forks
1 watermarks-remover

A privacy-first app that strips AI watermarks from content you own.

19.2K 2.2K
2 PySyft

Perform data science on data that remains in someone else's server

10.0K 2.0K
3 presidio

An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.

10.7K 1.3K
4 flower

Flower: A Friendly Federated AI Framework

7.1K 1.2K
5 ai.robots.txt

A list of AI agents and robots to block.

4.1K 179
6 deep-prove

Framework to prove inference of ML models blazingly fast

3.4K 102
7 privacy

Library for training machine learning models with privacy for training data

2.0K 474
8 secretflow

A unified framework for privacy-preserving data analysis and machine learning

2.7K 469
9 FedML

Unified scalable ML library for distributed training, federated learning, and model serving across clouds and edge devices.

4.1K 765
10 agi

The first distributed AGI system. Thousands of autonomous AI agents collaboratively train models, share experiments via P2P gossip, and push breakthroughs here. Fully peer-to-peer. Join from your browser or CLI.

2.0K 241
11 opacus

Training PyTorch models with differential privacy

2.0K 397
12 FATE

An Industrial Grade Federated Learning Framework

6.1K 1.6K
13 Awesome-FL

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

2.0K 223
14 nesa

Run AI models end-to-end encrypted.

3.2K 246