Edge Computing, Explained

The video introduces edge computing, explaining its role in local computation on mobile devices while connected to cloud servers. It contrasts edge computing with cloud computing, highlighting issues like latency, bandwidth, and privacy. The video discusses how edge computing can mitigate these challenges, using examples like the Apple M1 chip and federated learning in Siri. It emphasizes the potential of edge computing to enhance machine learning applications by reducing latency and improving data privacy.

What is the primary characteristic of edge computing?

• It is only used in desktop computers.

• It relies entirely on cloud servers.

• It processes data locally on devices.

• It requires a constant internet connection.

What was a major advantage of cloud computing over traditional desktop computing?

• Real-time access to larger systems.

• Enhanced battery life of devices.

• Increased local storage capacity.

• Elimination of internet dependency.

Which issue is NOT typically associated with cloud computing?

• Bandwidth

• Latency

• Increased local processing power

• Privacy

Why is latency a concern for autonomous vehicles using cloud computing?

• It improves data storage capacity.

• It increases the vehicle's speed.

• It delays decision-making processes.

• It enhances the vehicle's battery life.

How does edge computing improve privacy?

• By eliminating data encryption.

• By storing all data in the cloud.

• By processing data locally on devices.

• By sharing data with multiple servers.

Which technology is an example of hardware supporting edge computing?

• Desktop processors

• Cloud-based servers

• Traditional hard drives

• Apple M1 chip

What is a benefit of using Federated Learning in edge computing?

• It sends all data to the cloud.

• It reduces the need for local processing.

• It trains models locally without sending raw data.

• It increases the bandwidth requirements.