An open framework for Federated Learning.
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Updated
Oct 9, 2024 - Jupyter Notebook
An open framework for Federated Learning.
Federated Learning Utilities and Tools for Experimentation
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.
(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.
A Comprehensive and Versatile Open-Source Federated Learning Framework
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.
Implementation of the FedPM framework by the authors of the ICLR 2023 paper "Sparse Random Networks for Communication-Efficient Federated Learning".
Docker CLI package for the vantage6 infrastructure
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
BurrMill core
A project for simulation of Asynchronous Federated Learning
A Federated Learning based Android Malware Classification System
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.
Sparse Convex Optimization Toolkit (SCOT)
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries at NeurIPS'21
[TMLR] CoDeC: Communication-Efficient Decentralized Continual Learning
FedStream: Prototype-Based Federated Learning on Distributed Concept-drifting Data Streams
Official implementation of Deep Class Incremental Learning from Decentralized Data (IEEE TNNLS 22)
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