Summary

In this chapter, we discussed FL client-side implementation. There are three basic but important functionalities when participating in the FL process, receiving global models sent from an aggregator with a push or polling mechanism, and sending local models to an aggregator after the local training process. In order to effectively implement the client-side ML engines that cooperate with the FL aggregator, understanding the client state is important. The client states include a state waiting for the global model, a state indicating that local training is happening, a state showing the readiness to send local models, and a state for having the updated global models.

We also discussed the design philosophy of FL client-side libraries, where the core functions need to be effectively packaged to provide user-friendly interfaces for ML developers and engineers.

Last but not least, we learned how to actually use the FL client libraries to integrate a local ML engine into an...