Federated Learning is a distributed machine learning technique that enables model training across multiple participants — devices, organizations, or data centers — without requiring raw data to leave its original location. Instead of centralizing data, federated learning sends the model to the data, aggregates learned updates, and produces an improved global model.
The federated learning process works as follows: a central server distributes a global model to participating nodes, each node trains the model on its local data, nodes send only model updates (gradients or weights) back to the server — not the raw data, the server aggregates updates from all nodes to improve the global model, and the cycle repeats until the model converges.
Key benefits for enterprise AI include: data privacy preservation (sensitive data never leaves its source), regulatory compliance (enables AI training without cross-border data transfers), competitive collaboration (organizations can jointly train models without sharing proprietary data), reduced data centralization risk (no single point of data compromise), and edge AI training (models can learn from data on mobile devices, IoT sensors, and edge infrastructure).
Challenges include: communication overhead (transmitting model updates across networks), heterogeneous data (non-uniform data distributions across participants), security risks (model updates can leak information through inference attacks), and coordination complexity. Privacy-enhancing techniques like differential privacy and secure aggregation are often combined with federated learning for stronger protections.
