Lecture
Decentralized ML: Collaborative Training & Causal Influence Structure
Description
This lecture covers steps towards collaborative and decentralized machine learning, focusing on communication efficiency, gradient compression, and error feedback for model updates. It also explores building blocks like robustness, privacy, and efficiency. Additionally, it delves into causal influence structure discovery using Bayesian networks, directed information graphs, and beyond single-domain observational setups.
Official source
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