Federated Stack
The Federated Stack refers to a layered, distributed computing architecture where data processing and model training occur locally across multiple independent nodes or devices, rather than being aggregated into a single central repository. This structure allows organizations to leverage collective data insights while maintaining strict data sovereignty and privacy.
In an era of stringent data regulations (like GDPR and CCPA), centralizing sensitive data is a significant compliance risk. The Federated Stack addresses this by bringing the computation to the data. This shift is crucial for industries handling highly sensitive information, such as healthcare, finance, and IoT, enabling powerful AI development without compromising privacy.
The process typically involves a central orchestrator coordinating the training process. Local nodes (e.g., individual hospital servers or user devices) train a model using their proprietary local data. Instead of sharing the raw data, these nodes only share model updates or gradients with the central server. The central server then aggregates these updates to create an improved global model, which is then redistributed for the next round of local training.
This concept is closely related to Differential Privacy (which adds noise to updates for further privacy guarantees) and Edge Computing (which focuses on processing data near the source).
