Cross-Silo vs. Cross-Device Federated Learning

Cross-Silo Federated Learning

Cross-Silo Federated Learning involves a small number of reliable and stable participants (silos) such as organizations or institutions. These silos typically have significant computational resources and stable network connections.

Key Features:

• Few Participants: Involves a limited number of trusted entities.
• Stable Environment: Participants have stable and reliable computational and network resources.
• Large Datasets: Each participant usually possesses large amounts of data.

Example Use Case: Universities collaborating on a research project where each university has its dataset but wants to build a common predictive model.

Cross-Device Federated Learning

Cross-Device Federated Learning involves a large number of devices such as smartphones, IoT devices, or edge devices. These devices are typically less reliable and have varying computational capabilities and intermittent network connectivity.

Key Features:

• Many Participants: Involves a large number of devices.
• Unstable Environment: Devices may have limited computational resources and unstable network connections.
• Small Datasets: Each device typically has a small amount of data.

Example Use Case: Training a predictive text input model on mobile devices where each device contributes to the model without sending user data to a central server.

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.