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Artificial intelligence depends on data. But what happens when useful data cannot be collected, centralized, or freely shared?
Federated Learning offers a different approach. Instead of bringing all data into one central location, machine learning models can be trained across distributed devices, systems, and organizations while keeping much of the underlying data where it is generated.
At the same time, modern AI increasingly faces another challenge: obtaining enough high-quality data to train reliable models. Data generation-through simulations, augmentation, synthetic datasets, and distributed sources-can help expand what is available without relying entirely on centralized data collection.
Federated Learning offers a different approach. Instead of bringing all data into one central location, machine learning models can be trained across distributed devices, systems, and organizations while keeping much of the underlying data where it is generated.
At the same time, modern AI increasingly faces another challenge: obtaining enough high-quality data to train reliable models. Data generation-through simulations, augmentation, synthetic datasets, and distributed sources-can help expand what is available without relying entirely on centralized data collection.