Designing Federated Learning Architecture

Overview

Federated Learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their raw data. Each participant trains a local model on their own data and shares only model updates (gradients or parameters) with a central aggregator. The aggregated model benefits from all participants' data without any single party accessing another's dataset.

FL directly supports GDPR Article 5(1)(c) data minimization (only model updates are shared, not personal data), Article 25(1) data protection by design (privacy is built into the architecture), and can reduce the need for cross-border data transfers under Chapter V (data stays in its jurisdiction of origin).

FL Architectures

Horizontal Federated Learning

Participants share the same feature space but have different data samples. Each participant has a complete record for their subjects but covers different subjects.