VentureBeat October 26, 2024
Each year, cyberattacks become more frequent and data breaches become more expensive. Whether companies seek to protect their AI system during development or use their algorithm to improve their security posture, they must alleviate cybersecurity risks. Federated learning might be able to do both.
What is federated learning?
Federated learning is an approach to AI development in which multiple parties train a single model separately. Each downloads the current primary algorithm from a central cloud server. They train their configuration independently on local servers, uploading it upon completion. This way, they can share data remotely without exposing raw data or model parameters.
The centralized algorithm weighs the number of samples it receives from each disparately trained configuration, aggregating them to...
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