Part 3: Hands-On Federated Learning

This part covers the need for federated learning (FL) and the implementation of FL using open source frameworks. It also touches upon FL benchmarks, start-ups, and future opportunities in the field.

We highlight the importance of FL as a privacy-preserving approach to machine learning and emphasize the need for FL in scenarios where data cannot be centrally aggregated due to privacy concerns or regulatory restrictions. Furthermore, we discuss the implementation of FL using open source frameworks, which provide accessible and customizable tools for deploying FL algorithms and models.

We explore the significance of FL benchmarks for evaluating and comparing FL algorithms and techniques and emphasize the need for standardized benchmarks to assess the performance and effectiveness of FL models across different scenarios. By leveraging FL benchmarks, researchers and practitioners can identify the strengths and limitations of various FL approaches...