Practical Guide to Deploying Federated Learning: Architecture, Deployment, Training, and Inference

Federated Learning (FL) is a distributed machine learning paradigm that protects data privacy by transmitting model parameters instead of raw data, enabling collaborative model training across heterogeneous devices while keeping most data locally.

The article first discusses deployment strategies, recommending packaging the FL application and its dependencies into a lightweight Docker container to achieve portable, fast, and consistent execution across diverse environments, similar to shipping containers for ships.

Key advantages of Docker‑based deployment include rapid startup (seconds or milliseconds), reduced disk usage (MB‑level versus GB‑level VMs), simplified environment setup, consistent runtime across machines, easier CI integration, and support for SOA or micro‑service architectures.

Time and cost savings

Environment consistency

CI/CD friendliness

Loose coupling for service orchestration

Cross‑platform publishing

The training framework section outlines the necessary services: a communication gateway exposing gRPC/HTTP APIs, a training service (with validation, scheduling, metadata management, and FL components), a model management service for persistence, and a registration center (e.g., ZooKeeper) for high‑availability service discovery.

A typical training workflow includes submitting a task to the gateway, parameter validation, loading heterogeneous data sources (CSV, HDFS, MySQL), intersecting feature sets across parties, executing federated algorithms (e.g., LR, DNN), evaluating models (AUC, KS), and storing the final model.

The inference framework mirrors the training architecture but adds real‑time performance requirements, monitoring, and version control. It consists of a communication proxy, an inference service (registering with ZooKeeper, loading models from distributed storage, performing predictions), a model management service, and a storage service for prediction results.

The inference workflow involves routing a request through the gateway, fetching or loading the appropriate model, preprocessing features on both parties, executing the prediction, and post‑processing the results before persisting them.

Overall, the article emphasizes balancing flexibility and convenience when designing FL systems, addressing challenges such as heterogeneous environments, fault tolerance, continuous integration, and high‑availability deployment.