Popular GitHub Repositories for Privacy Preserving Machine Learning

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

Training PyTorch models with differential privacy

A Privacy-Preserving Framework Based on TensorFlow

Fast, memory-efficient, scalable optimization of deep learning with differential privacy

This repository contains all the implementation of different papers on Federated Learning

[ICML 2022 / ICLR 2024] Source code for our papers "Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks" and "Be Careful What You Smooth For".

Federated Learning with Differential Privacy and Homomorphic Encryption.

Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold t must be greater than half of the number of users. The assumption is so rigorous that in some scenarios the schemes may not be appropriate. Motivated by the issue, we first introduce membership proof for federated learning, which leverages cryptographic accumulators to generate membership proofs by accumulating users IDs. The proofs are issued in a public blockchain for users to verify. With membership proof, we propose a privacy-preserving federated learning

[ECCV 2022] Official pytorch implementation of the paper "FedVLN: Privacy-preserving Federated Vision-and-Language Navigation"

[TOIS] "Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning"

privacy preserving recommendation system research project as research engineer of https://www.openmined.org community

FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.

FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.

Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks

FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.

Reupload of my project leveraging Federated Learning on Smart Grid data.

Implementation of the Heflp, a framework enabling practical and overflow-safe federated learning.

Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).