poga/awesome-federated-learning
resources about federated learning and privacy in machine learning
This is a curated collection of research papers and resources related to federated learning and privacy in machine learning. It provides insights into how to build and deploy machine learning models using decentralized data, ensuring the privacy of sensitive information. Data scientists, machine learning engineers, and researchers who are developing models for industries with strict data privacy regulations, such as healthcare or finance, would find this useful.
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Use this if you need to research or implement machine learning solutions that require collaboration across multiple data sources without directly sharing raw, sensitive data.
Not ideal if you are looking for an off-the-shelf software tool or code library to directly apply to a dataset.
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Jun 26, 2024
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