Federated Learning

Federated learning is a distributed machine learning technique that enables model training across multiple data holders, such as organizations, devices, or geographic locations, without requiring the raw data to leave its original location. Instead of centralizing data for training, federated learning sends the model to the data, computes local updates, and aggregates only the model parameter changes, preserving data privacy and sovereignty.

The standard federated learning process follows a cycle: a central server distributes the current global model to participating clients (devices or organizations), each client trains the model on its local data to produce updated parameters, clients send only their parameter updates (gradients or weight differences) back to the server, the server aggregates updates using algorithms like Federated Averaging (FedAvg) to produce an improved global model, and the cycle repeats. The raw data never leaves the client, addressing privacy and data sovereignty concerns.

Federated learning faces several technical challenges. Data heterogeneity across clients (non-IID data) can cause the global model to converge slowly or perform poorly on individual clients. Communication efficiency is critical when training across many devices with limited bandwidth. System heterogeneity means clients have different computational capabilities and availability. Security concerns include the potential for gradient-based data reconstruction attacks, mitigated through techniques like secure aggregation and differential privacy.

Enterprise applications of federated learning include collaborative model training across hospitals without sharing patient records (preserving HIPAA compliance), cross-organization fraud detection in financial services without exposing transaction data, keyboard prediction models trained on device usage without uploading personal typing data, and multi-national AI development that complies with data localization laws. Federated learning enables organizations to benefit from collective data without compromising individual data governance requirements.