In this issue:

• Applying deferential privacy to federated learning(FL) scenarios.

• Meta AI’s research about FL and deferential privacy.

• The best deferential privacy frameworks to use in FL architectures.

💡 ML Concept of the Day: Federated Learning and Differential Privacy

To conclude our series on federated learning, today we will cover its implications for private machine learning (pML). Privacy and federated learning often go together. In some sense, the entire federated learning discipline was created as an effort to have strong privacy constraints in mobile ML architectures. From that perspective, federated learning is often combined with security methods to enable sophisticated private ML applications. Among the techniques in this area, the combination of federated learning and differential privacy (DP) has seen the most relevant adoption.

What is DP? ...