QVQZZZ/HeFlwr
HeFlwr: Federated Learning for Heterogeneous Devices
This framework helps machine learning researchers and developers overcome the challenges of deploying federated learning models on diverse devices. It allows you to customize and manage machine learning models that adapt to varying computing power, storage, and network conditions across different devices. The framework takes in your federated learning setup and outputs models optimized for heterogeneous hardware, along with detailed resource usage metrics for each device.
125 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are a researcher or developer working with federated learning and need to test or deploy models in real-world environments with devices that have significantly different hardware capabilities.
Not ideal if you are looking for a simple, out-of-the-box solution for federated learning without needing to customize models for diverse device capabilities or monitor system resources.
Stars
125
Forks
13
Language
Python
License
MIT
Category
Last pushed
Mar 03, 2025
Commits (30d)
0
Dependencies
2
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