Centralized Federated Learning using WebSockets and TensorFlow
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Updated
Jan 21, 2022 - Python
Centralized Federated Learning using WebSockets and TensorFlow
Simulation Codes for “Relay-Assisted Cooperative Federated Learning”
Decentralized (P2P) Federated Learning implementation using libp2p JavaScript
Page for fedsim
Guide (based on my experiments) on NVIDIA FLARE™ (NVIDIA Federated Learning Application Runtime Environment)
🔨 A Flexible Federated Learning Simulator for Heterogeneous and Asynchronous.
This repo is about federated learning implementation using FLOWER framework for beginners.
A research-oriented federal learning framework.
FedKeeper is a client-based python tool for propagating FL-client functions over FaaS fabric. Its main objective is to act as a manager or keeper of various client functions distributed over different FaaS platforms.
A framework for fast federated learning algorithm verification based on Tensorflow (Only suitable for academic research)
Galaxy Federated Learning Framework (星际联邦学习框架)
[ICLR2023] Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning (https://arxiv.org/abs/2210.00226)
A Federated Deep Learning Simulator Framework (FedSim). This simulator was developed under supervision of Dr. Keyvan RahimiZadeh.
All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
FedRay: a Research Framework for Federated Learning based on Ray
Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.
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.
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
A Docker-Based Federated Learning Framework Design and Deployment for Multi-modal Data Stream Classification
An Efficient and Easy-to-use Federated Learning Framework.
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