July 8, 2021
11:00 am / 12:30 pm
Federated learning (FL) is a machine learning paradigm where many clients (e.g. mobile devices or whole organizations) collaboratively train model while keeping their data decentralized. FL embodies the principals of focused data collection and minimization, and can mitigate many of the systematic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. In this talk, I will introduce various settings which fall under the umbrella of FL, review a few standard algorithms and discuss some recent work and open problems.