Federated Learning Utilities and Tools for Experimentation
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Updated
Jan 11, 2024 - Python
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.
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
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".
Docker CLI package for the vantage6 infrastructure
A Federated Learning based Android Malware Classification System
A project for simulation of Asynchronous Federated Learning
BurrMill core
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.
Papers related to Federated Learning in all top venues
Sparse Convex Optimization Toolkit (SCOT)
[TMLR] CoDeC: Communication-Efficient Decentralized Continual Learning
(CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning"
Simple Implementation of the CVPR 2024 Paper "JointSQ: Joint Sparsification-Quantization for Distributed Learning"
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries at NeurIPS'21
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