Centralized vs. Decentralized Federated Learning

Centralized Federated Learning

Centralized Federated Learning involves a central server that orchestrates the training process. Participants (clients) train local models on their data and send the updated model parameters to the central server. The server aggregates these parameters to update the global model, which is then distributed back to the clients.

Key Features:

• Coordination: The central server coordinates the training, aggregation, and distribution processes.
• Aggregation: Model updates are aggregated at a single point, simplifying the update process.
• Control: The central server has control over the training process, making it easier to manage.

Example Use Case: A healthcare consortium where hospitals train models locally on patient data and a central server aggregates these models to improve disease prediction models.

Decentralized Federated Learning

Decentralized Federated Learning eliminates the need for a central server. Instead, participants communicate directly with each other to share and aggregate model updates. This peer-to-peer communication ensures that there is no single point of failure and enhances privacy by distributing the aggregation process.

Key Features:

• No Central Server: Participants communicate directly, reducing the reliance on a central authority.
• Robustness: The absence of a central server reduces the risk of a single point of failure.
• Privacy: Enhanced privacy as aggregation is distributed across participants.

Example Use Case: A network of mobile devices collaboratively training a model for predicting app usage patterns without relying on 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.