Differential Privacy + Federated Learning Explained (+ Tutorial) | #AI101

The video discusses privacy concerns in AI, focusing on Differential Privacy and Federated Learning. Differential Privacy involves adding noise to data to protect individual identities, while Federated Learning trains models across separate data sets without sharing data. The video includes a coding tutorial using Google Collab to experiment with these privacy techniques.

What are the two main approaches to protect user privacy in machine learning?

• Encrypting data and using secure servers

• Using firewalls and antivirus software

• Data compression and encryption

• Differential Privacy and Federated Learning

In Differential Privacy, what does the parameter epsilon control?

• The speed of data processing

• The size of the data set

• The degree of privacy versus accuracy

• The level of data encryption

Why is Differential Privacy particularly useful for medical data?

• It allows sharing of data without revealing patient identities

• It increases the speed of data processing

• It reduces the cost of data storage

• It improves the accuracy of medical predictions

What is the purpose of adding noise in Differential Privacy?

• To increase data processing speed

• To make data storage more efficient

• To obscure individual data points for privacy

• To enhance data visualization

What is the primary focus of Federated Learning?

• Reducing the size of data sets

• Training algorithms across multiple data sets without sharing data

• Encrypting data before processing

• Improving the speed of data processing

How does Federated Learning benefit personal devices like smartphones?

• By enhancing internet connectivity

• By increasing storage capacity

• By improving voice recognition without sharing personal data

• By reducing battery consumption

What tool is recommended for experimenting with AI privacy techniques at home?

• Microsoft Excel

• Amazon Web Services

• Adobe Photoshop

• Google Collab