Fairness and Robustness in Federated Learning with Virginia Smith -#504

Fairness and Robustness in Federated Learning with Virginia Smith -#504

Today we kick off our ICML coverage joined by Virginia Smith, an assistant professor in the Machine Learning Department at Carnegie Mellon University. In our conversation with Virginia, we explore her work on cross-device federated learning applications, including where the distributed learning aspects of FL are relative to the privacy techniques. We dig into her paper from ICML, Ditto: Fair and Robust Federated Learning Through Personalization, what fairness means in contrast to AI ethics, the particulars of the failure modes, the relationship between models, and the things being optimized across devices, and the tradeoffs between fairness and robustness. We also discuss a second paper, Heterogeneity for the Win: One-Shot Federated Clustering, how the proposed method makes heterogeneity beneficial in data, how the heterogeneity of data is classified, and some applications of FL in an unsupervised setting. The complete show notes for this episode can be found at twimlai.com/go/504.

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This Week in Machine Learning & AI brings you the week's most interesting and important stories from the world of machine learning and artificial intelligence. This week's episode explores the White House workshops on AI, human bias in AI and machine learning models, a company working on machine learning for small datasets, plus the latest AI & ML news and a self-driving car that learned how to drive aggressively.

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This Week In Machine Learning & AI - 5/20/16: AI at Google I/O, Amazon's Deep Learning DSSTNE

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