Previous chapters concentrated on the algorithms and mathematical principles of advanced federated learning, including aggregation strategies and privacy enhancements. This chapter transitions to the practical engineering required to build, deploy, and manage these systems effectively.
We will examine the common architectural patterns of FL systems, introduce key frameworks like TensorFlow Federated, PySyft, and Flower that facilitate development, and discuss the distinct challenges of moving from controlled simulations to real-application deployments. Additionally, you will learn about monitoring techniques and security measures pertinent to operational federated learning setups, covering both cross-silo and cross-device scenarios. By the end of this chapter, you will have a structured understanding of how to approach the construction and maintenance of federated learning infrastructure.
6.1 Architecture of Federated Learning Systems
6.2 Overview of FL Frameworks
6.3 Simulation vs. Deployment
6.4 Monitoring and Debugging Federated Systems
6.5 Cross-Silo vs. Cross-Device FL Implementations
6.6 Security Considerations in System Deployment
6.7 Practice: Setting up a Basic FL Simulation
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