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YouTube

Leveraging Spatial and Temporal Correlations in Distributed Learning - Seminar 91

Federated Learning One World Seminar via YouTube

Overview

Watch a 56-minute FLOW seminar presentation where Carnegie Mellon University's Gauri Joshi explores the optimization of distributed learning systems by leveraging spatial and temporal correlations. Dive into federated learning concepts, starting with training at the edge and progressing through federated optimization objectives and the FedAvg algorithm. Learn about distributed mean estimation techniques, spatial similarity leveraging, and Rand-k sparsification methods. Explore practical applications through case studies including distributed power iteration, K-means clustering, and logistic regression. Examine the impact of intermittent client participation on FedAvg convergence while gaining insights into measuring spatial correlation and implementing various spatial estimators. Originally presented on January 18th, 2023, as part of the Federated Learning One World Seminar series, this technical talk provides a comprehensive exploration of advanced distributed learning optimization strategies.

Syllabus

Intro
Federated Learning: Bringing Training to the Edge
Federated Optimization: Objectives and Notation
Federated Optimization: The FedAvg Algorithm
Distributed Mean Estimation
Leveraging Spatial Similarity
Rand-k Sparsification
How do we measure spatial correlation?
Family of Spatial Estimators for different p
Talk Outline
Proposed Rand-k-Temporal Estimator
Quadratic Objective Case Study
Distributed Power Iteration
Distributed K-Means
Distributed Logistic Regression
Error of Intermittent Client Participation on the convergence of FedAvg
Conclusion

Taught by

Federated Learning One World Seminar

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