82 Open source projects that are alternatives of or similar to federated

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

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage…

Healthcare-Researcher-Connector Package: Federated Learning tool for bridging the gap between Healthcare providers and researchers

Proof of Concept of a Federated Learning framework that maintains the privacy of the participants involved.

Full stack service enabling decentralized machine learning on private data

Federated Principal Component Analysis Revisited!

Awesome list for cryptographic secure computation paper. This repo includes *Lattice*, *DifferentialPrivacy*, *MPC* and also a comprehensive summary for top conferences.

Fit interpretable models. Explain blackbox machine learning.

testing out homomorphic encryption

A toolbox for differentially private data generation

Easy differential privacy in R

Bridge between TensorFlow and the Microsoft SEAL homomorphic encryption library

Additive homomorphic EC-ElGamal

Privacy-Preserving Data Analysis using Pandas

iOS SDK of WeDPR-Lab-Core; WeDPR即时可用场景式隐私保护高效解决方案核心算法组件iOS SDK

Tools and service for differentially private processing of tabular and relational data

Implementing Homomorphic Encryption in Golang 🌱

Symmetric somewhat homomorphic encryption library based on DGHV

Locally Private Graph Neural Networks (ACM CCS 2021)

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

Proof Of Concept of DEcentralised Machine Learning on top of the Golem (https://golem.network/) architecture

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

A curated list of references for MLOps

Core libraries of WeDPR instant scenario-focused solutions for privacy-inspired business; WeDPR即时可用场景式隐私保护高效解决方案核心算法组件

电力人工智能数据竞赛——液压吊车目标检测赛道

Federated gradient boosted decision tree learning

Android SDK of WeDPR-Lab-Core; WeDPR即时可用场景式隐私保护高效解决方案核心算法组件Android SDK

The Intel Homomorphic Encryption (HE) toolkit is the primordial vehicle for the continuous distribution of the Intel HE technological innovation to users. The toolkit has been designed with usability in mind and to make it easier for users to evaluate and deploy homomorphic encryption technology on the Intel platforms.

Google's differential privacy libraries.

Homomorphic Encryption for TypeScript or JavaScript - Microsoft SEAL

Naive implementation of basic Differential-Privacy framework and algorithms

Threshold Signature Scheme for ECDSA

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/1610.05755)

Privacy -preserving Neural Networks

A scalable, high-performance serving system for federated learning models

FedScale is a scalable and extensible open-source federated learning (FL) platform.

Concrete Numpy is a python package that contains the tools data scientists need to compile various numpy functions into their Fully Homomorphic Encryption (FHE) equivalents. Concrete Numpy goes on top of the Concrete Library and its Compiler.

A machine-learning-based tool for discovering differential privacy violations in black-box algorithms.

The core library of differential privacy algorithms powering the OpenDP Project.

Extension for the .NET Framework cryptography subsystem, which introduces the ElGamal public key cryptosystem with support for homomorphic multiplication.

Federated Learning mini-framework with Keras

Hääl - Anonymous Electronic Voting System on Public Blockchains

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

A library for answering questions using data you cannot see

A pure-Rust implementation of the Paillier encryption scheme

An Industrial Grade Federated Learning Framework

Source code for 'Dual Attention Based FL for Wireless Traffic Prediction'

The repo for the FeTS Challenge

A Java library for Paillier partially homomorphic encryption.

Federated Learning based Deep Learning. Docs: https://fets-ai.github.io/Front-End/

Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)

An experimental platform to quickly realize and compare with popular centralized federated learning algorithms. A realization of federated learning algorithm on fairness (FedFV, Federated Learning with Fair Averaging, https://fanxlxmu.github.io/publication/ijcai2021/) was accepted by IJCAI-21 (https://www.ijcai.org/proceedings/2021/223).

Libraries and tools to perform fully homomorphic encryption operations on an encrypted data set.

Standard federated learning implementations in FedLab and FL benchmarks.

Microsoft SEAL is an easy-to-use and powerful homomorphic encryption library.

Here is the official implementation of the model KD3A in paper "KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation".

CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)

The implementation of "Towards Faster and Better Federated Learning: A Feature Fusion Approach" (ICIP 2019)

FedDANE: A Federated Newton-Type Method (Asilomar Conference on Signals, Systems, and Computers ‘19)

Java SDK of WeDPR-Lab-Core; WeDPR即时可用场景式隐私保护高效解决方案核心算法组件通用Java SDK