Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi
SRDS 2020: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
This project helps evaluate and compare different distributed machine learning approaches like federated learning, split learning, and ensemble learning on Internet of Things (IoT) devices. It takes pre-processed datasets, such as medical data like the MIT arrhythmia ECG database, and allows you to run distributed training across a central server and multiple client devices, including Raspberry Pis. This is designed for researchers and engineers working with IoT and edge AI, who need to understand the performance and resource usage of these distributed models.
118 stars. No commits in the last 6 months.
Use this if you are a researcher or engineer looking to test and evaluate distributed machine learning models on resource-constrained IoT devices like Raspberry Pis.
Not ideal if you are looking for a high-level SDK for building production-ready distributed AI applications without needing to delve into the underlying implementation and evaluation.
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MIT
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Last pushed
Mar 15, 2021
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