Distributed Machine Learning: Algorithms and Challenges

This lecture by the instructor covers the topic of distributed machine learning, focusing on algorithms and challenges. The presentation starts with an overview of Byzantine-Worker-Tolerant ML, discussing various works related to machine learning with adversaries. The lecture then delves into the reasons why adaptive methods are beneficial for attention models, providing empirical and theoretical evidence to support the claims. The concept of collaborative learning and average agreement is explained, along with the theorem stating the equivalence between C-collaborative learning and C-averaging agreement. The presentation concludes with open problems in the field of distributed machine learning.