Serverless-Federated-Learning/FedLess
Secure and Scalable Federated Learning using Serverless Computing
This project helps machine learning engineers and researchers conduct federated learning experiments more securely and scalably. It takes various machine learning models and datasets, distributing the training across multiple serverless functions (clients) to aggregate a final, collaboratively trained model. It's designed for those who need to train models on decentralized data without moving raw data.
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Use this if you need to train machine learning models on data distributed across many different locations or organizations, ensuring data privacy and efficient scaling.
Not ideal if you have all your data centralized and want to perform traditional, single-location model training.
Stars
12
Forks
6
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 31, 2024
Commits (30d)
0
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