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1

Appfl

Advanced Privacy-Preserving Federated Learning framework

APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups.

Downloads: 7 This Week

Last Update: 2026-03-09
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2

NErlNet

Nerlnet is a framework for research and development

NErlNet is a research-grade framework for distributed machine learning over IoT and edge devices. Built with Erlang (Cowboy HTTP), OpenNN, and Python (Flask), it enables simulation of clusters on a single machine or real deployment across heterogeneous devices.

Downloads: 0 This Week

Last Update: 2025-12-30
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3

FedLab

A flexible Federated Learning Framework based on PyTorch

A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes.

Downloads: 5 This Week

Last Update: 2025-07-15
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4

Fedhf

A Flexible Federated Learning Simulator

FedHF is a Python-based simulator for flexible, heterogeneous, and asynchronous federated learning research. It provides configurable resource models, supports asynchronous protocols, and accelerates experimentation.

Downloads: 8 This Week

Last Update: 2025-07-15
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