Federated learning is an approach that allows multiple devices or servers to collaboratively train a model while keeping their data localized. Common architectures used in federated learning systems primarily include the client-server model, peer-to-peer (P2P) architecture, and hierarchical federated learning. Each model has its unique benefits and use cases, making them suitable for different applications and environments.
In the client-server model, a central server coordinates the training process among participating clients, such as mobile devices or edge devices. Clients locally train the model using their data and periodically send their model updates (like gradients) back to the server. The server aggregates these updates through techniques such as averaging to form a global model. This architecture is widely used due to its simplicity and effectiveness, especially in applications like personalizing mobile applications, where users' data remains on their devices, ensuring privacy while improving model performance.
The peer-to-peer architecture allows clients to share updates directly with each other instead of relying on a central server. In this setup, participating devices can collaborate and exchange model parameters directly, fostering a decentralized approach. This model can enhance scalability and reduce latency, making it suitable for systems with numerous clients or in environments with unpredictable server availability. Lastly, hierarchical federated learning adds another layer by dividing clients into groups or clusters. Each cluster may have its local server that aggregates updates before sending them to a higher-level server, optimizing the aggregation process and improving communication efficiency in large-scale environments. This architecture is particularly beneficial in scenarios where data distribution is non-uniform, like in healthcare applications involving various institutions.