Distributed Machine Learning with Python

Chapter 10: Federated Learning and Edge Devices

When discussing DNN training, we mainly focus on using high-performance computers with accelerators such as GPUs or traditional data centers. Federated learning takes a different approach, trying to train models on edge devices, which usually have much less computation power compared with GPUs.

Before we discuss anything further, we want to list our assumptions:

• We assume the computation power of mobile chips is much less than traditional hardware accelerators such as GPUs/TPUs.
• We assume mobile devices often have a limited computation budget due to the limited battery power.
• We assume the model training/serving platform for a mobile device will be different from the model training/serving platform ...