Machine learning without sharing data using Federated Learning
Training a machine learning model without sharing data using Federated Learning
In the fast-evolving field of machine learning, data privacy and security are major concerns. Traditional centralized approaches to machine learning require consolidating all data into one location for model training. This approach, however, carries significant privacy risks and often encounters regulatory challenges, particularly in sensitive sectors such as healthcare and maritime operations.
In the fast-evolving field of machine learning, data privacy and security are major concerns. Traditional centralized approaches to machine learning require consolidating all data into one location for model training. This approach, however, carries significant privacy risks and often encounters regulatory challenges, particularly in sensitive sectors such as healthcare and maritime operations.
Federated Learning
Federated Learning harnesses the collective intelligence of multiple decentralized devices or servers, enabling them to collaboratively train a shared model while keeping data localized. This innovative method, popularized by Google, not only reduces privacy risks but also improves the robustness and accuracy of machine learning models by utilizing diverse data sources. By preserving data privacy and security, Federated Learning complies with regulatory requirements and standards, making it an appealing choice for industries that manage sensitive information.
For a complete project description please see download below. Here we compare three configurations for training a neural network. We demonstrate that using a Federated Learning approach can significantly enhance the prediction accuracy of models trained without data sharing.
For a complete project description please see download below. Here we compare three configurations for training a neural network. We demonstrate that using a Federated Learning approach can significantly enhance the prediction accuracy of models trained without data sharing.
Contact
Gaspard Ducamp
Data Scientist