The Sequence Scope: The Most Important Federated Learning Framework

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📝 Editorial: The Most Important Federated Learning Framework

Federated learning is often regarded as one of the most important machine learning (ML) techniques for privacy and one of the cornerstones of mobile ML (we covered it in Edge#5). The core idea behind federated learning is that multiple agents (think mobile devices) can collaborate in mastering a specific task without relying on centralized training data. Think about a mobile ML models distributed in a mobile app across millions of devices. Ideally, the model can benefit from the data produced by each instance of the app, but that entails very concerning privacy vulnerabilities. Federated learning enables a way in which only updates on the model are distributed to a centralized location while the training data remains on the device.

Since Google pioneered the idea of federated learning in 2017, it has become one of the most important methods for secured learning across many agents. However, federated learning…