How does OpenFL (Open Federated Learning) work?

OpenFL (Open Federated Learning) is a framework designed to enable decentralized machine learning across multiple organizations or devices while keeping data localized. It works by coordinating a central “Aggregator” and distributed “Collaborators” (e.g., hospitals, edge devices) that train shared models without sharing raw data. The Aggregator manages the global model and aggregates updates from Collaborators, which perform local training on their private datasets. This approach preserves privacy and complies with data regulations like GDPR, as raw data never leaves the original location.

Technically, OpenFL uses a task-based workflow. The Aggregator initializes a global model and distributes it to Collaborators. Each Collaborator trains the model locally using its dataset, computes model updates (e.g., gradients or weights), and sends these updates back to the Aggregator. The Aggregator then combines the updates—often using algorithms like Federated Averaging (FedAvg)—to create an improved global model. For example, in a healthcare scenario, five hospitals could collaboratively train a tumor-detection model: each hospital trains on its patient scans locally, and only model updates (not patient data) are shared. OpenFL handles communication via protocols like gRPC or HTTP and includes security features like secure aggregation to prevent leakage of sensitive information from model updates.

Developers implement OpenFL by defining a Python-based training workflow. First, they create a “Plan” specifying the model architecture, optimizer, and data loader. Collaborators are configured via YAML files to point to their local datasets. The Aggregator orchestrates rounds of training by sending tasks (e.g., “train for 10 epochs”) to Collaborators and merging results. For instance, a developer might write a training script that uses PyTorch to compute local model updates on a GPU-enabled Collaborator node. OpenFL also supports plug-ins for differential privacy or custom aggregation rules. Challenges like network latency or heterogeneous data distributions are addressed through compression techniques (e.g., quantizing model updates) and robust aggregation methods. By abstracting communication and coordination, OpenFL simplifies building federated systems that scale across diverse hardware, from servers to IoT devices.