makgyver/gossipy
Python module for simulating gossip learning.
This tool helps researchers and practitioners in distributed machine learning explore and simulate 'gossip learning' and 'decentralized federated learning' systems. You input your machine learning models and data distribution scenarios across different nodes, and it simulates how these models learn and update by exchanging information (gossip) among themselves without a central server. The output provides insights into the performance and behavior of these decentralized learning approaches. It's ideal for those studying or implementing distributed AI, especially in privacy-sensitive or resource-constrained environments.
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Use this if you need to simulate and understand how machine learning models learn collaboratively across many separate computers or devices without sharing raw data with a central authority.
Not ideal if you are looking for a plug-and-play solution to deploy a centralized machine learning model or if you don't need to explore decentralized learning paradigms.
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40
Forks
15
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 25, 2024
Commits (30d)
0
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