An open framework for Federated Learning.
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Updated
Jun 15, 2024 - Jupyter Notebook
An open framework for Federated Learning.
FedGraphNN: A Federated Learning Platform for Graph Neural Networks with MLOps Support. The previous research version is accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.
Federated Learning Utilities and Tools for Experimentation
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"
Simulate collaborative ML scenarios, experiment multi-partner learning approaches and measure respective contributions of different datasets to model performance.
Docker CLI package for the vantage6 infrastructure
A Federated Learning based Android Malware Classification System
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
A Comprehensive and Versatile Open-Source Federated Learning Framework
Auto-Multilift is a novel learning framework for cooperative load transportation with quadrotors. It can automatically tune various MPC hyperparameters, which are modeled by DNNs and difficult to tune manually, via reinforcement learning in a distributed and closed-loop manner.
A project for simulation of Asynchronous Federated Learning
Unofficial implementation of SignSGD to assess its robustness to adversaries.
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries at NeurIPS'21
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
Implementation of the FedPM framework by the authors of the ICLR 2023 paper "Sparse Random Networks for Communication-Efficient Federated Learning".
subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment
Sample PHT implementations efforts from the PHT German team
This is a distributed training framework for continual and incremental learning for multi-label multi-class image tasks
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