Horizontal vs. Vertical Federated Learning

Horizontal Federated Learning (HFL)

Horizontal Federated Learning (also known as Sample-based FL) occurs when datasets across different participants share the same feature space but differ in samples. Essentially, the datasets have the same structure (features) but contain data about different entities.

Key Features:

• Same Features: Datasets have the same features but different samples.
• Collaboration: Participants can collaboratively train a model without sharing their actual data.
• Applicability: Suitable for scenarios where different entities have similar data structures.

Example Use Case: Multiple banks training a fraud detection model on their transaction data without sharing the actual transaction records.

Vertical Federated Learning (VFL)

Vertical Federated Learning (also known as Feature-based FL) occurs when participants have datasets that share the same sample space but differ in features. This means that each participant holds different attributes about the same set of entities.

Key Features:

• Same Samples: Datasets have the same entities but different features.
• Feature Combination: Enables the combination of features from different participants to train a more comprehensive model.
• Privacy: Data privacy is maintained as participants only share intermediate computations.

Example Use Case: A partnership between a retail company and a bank where the retail company has purchasing data and the bank has financial data about the same customers.

Federated Learning is a powerful technique that allow a single machine to learn from many different source and converting the data into small pieces sending them to different Federated Learning (FL) is a decentralized of the machine learning paradigm that can enables to model training across various devices while preserving your data the data privacy.

In this article, we are going to learn about federated learning and discuss it’s types.