This post was originally published on Pure Storage
In an era where data privacy concerns are at the forefront, federated learning has emerged as a promising solution. Traditional machine learning models require massive amounts of centralized data for training, but that comes at a price—primarily in terms of data privacy and security. Federated learning, on the other hand, offers a way to train machine learning models without the need for centralized data collection.
Federated learning keeps sensitive data decentralized and local to the source. But as this approach gains traction, the conversation around its implications for data storage and privacy could get more complex.
What Is Federated Learning?
Federated learning is a distributed approach to machine learning where the model is trained across multiple decentralized devices or servers that hold their own local data. Instead of sending raw data to a central server for training, the process happens locally on each device. The local models are then aggregated at a central location, allowing the global model to improve without ever accessing the raw data itself. This method reduces data transmission and enhances privacy by keeping sensitive information close to the source.
Companies like Google have been early adopters of federated learning for applications like mobile keyboard predictions. Instead
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