FLEXible-FL/FLEXible
Federated Learning (FL) experiment simulation in Python.
This framework helps machine learning researchers and practitioners simulate federated learning experiments without moving sensitive data. You can feed in your own datasets and deep learning models (PyTorch or TensorFlow), define different roles like servers and clients, and then customize how they interact. The output is a simulated federated learning scenario, helping you understand and optimize distributed model training.
Use this if you need to design and test custom federated learning architectures and algorithms with your own datasets, ensuring data privacy and distributed computation.
Not ideal if you're looking for a simple, out-of-the-box federated learning solution for production deployment without significant customization needs.
Stars
22
Forks
1
Language
Python
License
AGPL-3.0
Category
Last pushed
Jan 07, 2026
Commits (30d)
0
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