Machine learning vs. federated learning

With the advancement of AIArtificial intelligence., we get to discover two techniques used to train models on large datasets:

• Machine learning

• Federated learning

Machine learning is a well-established method, whereas federated learning is an emerging way to solve several problems. This Answer focuses on distinguishing machine learning and federated learning by defining them and discussing their advantages and disadvantages.

What is machine learning?

Machine learning is a sub-branch of artificial intelligence that focuses on appending large datasets into an algorithm and helping computer systems implement the following functions:

• Learn from the data

• Identify patterns in the data

• Make predictions based on the given dataset

Machine learning involves the method of centralized dataWhere all the data is collected and analyzed at one location. storage and processing.Centralized

Machine learning can be classified into three types:

• Supervised learning: It involves the processing and prediction of data using labeled datasets.

• Unsupervised learning: It involves the processing and prediction of data using unlabeled datasets.

• Reinforcement learning: It consists of sensors, known as agents, that respond to the environment and receive rewards and punishment for their actions.

Some practical examples of machine learning are:

• Speech recognition

• Fraud detection

• Image recognition

Advantages

• Accurate prediction: It has a high potential for accurate prediction.

• Fast data processing: It quickly processes data due to a centralized system.

• Easy implementation: The open-source algorithms facilitate implementation.

Disadvantages

• Data security concerns: Having centralized data can cause data privacy issues.

• High computation cost:  It requires large amounts of time, resources, and speed while computing a large dataset.

Federated learning plays a significant role in overcoming these limitations caused by machine learning.

What is federated learning?

Federated learning is a machine learning technique that focuses on decentralizedData is divided into different nodes for storage and processing. processing by distributing the data across multiple computing devices and servers rather than bringing all the data to one central location.

The algorithm works collectively on the data stored on each device/server without sharing the data. After applying an algorithm to all the subsets of the data, each device sends the updated data to the head server that aggregates all the data to create an improved model. This method is also known as federated optimizationThe method of sending back data to a head node and combining the two to form improved data..

Some practical examples of federated learning are:

• Healthcare

• Autonomous vehicles

• Internet of Things (IoT)

Advantages

• More data security: It enhances data privacy by processing it locally.

• Reduced computational cost: It requires less time for processing.

• Robustness: It doesn't stop if any device fails.

Disadvantages

• Technical responsibility: Ensuring all the devices have the same capacity and model.

• Communication delays: It can cause a delay in model training if any device slows down.

• Expensive: It requires large amounts of devices.

Differences

Here are some key differences between machine learning and federated learning:

Summary

This Answer highlights the main differences between machine learning and federated learning by stating their definitions, advantages, disadvantages, and differences.