NaiboWang/Data-Free-Ensemble-Selection-For-One-Shot-Federated-Learning
Data-Free Ensemble Selection For One-Shot Federated Learning
This project helps machine learning practitioners in model marketplaces efficiently combine multiple pre-trained AI models without needing access to the original training data. It takes a collection of diverse machine learning models and outputs a smaller, more effective 'ensemble' of models that performs better than any single model. This is designed for AI researchers and engineers working with federated learning or model aggregation scenarios.
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Use this if you need to select the best combination of existing machine learning models to improve overall performance, especially when you cannot access the original training data for privacy or logistical reasons.
Not ideal if you are developing a brand new machine learning model from scratch or if you have full access to all the original training data and computational resources for traditional model training.
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Jul 26, 2024
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