25 Jun Wu Uiuc Personalized Federated Learning With Parameter Propagation

Main Takeaway: Symposium on Foundations of Responsible Computing (FORC) 2022 6/8/2022 Speaker: Shengyuan Hu, Carnegie Mellon ... A Google TechTalk, presented by Yanning Shen, University of California, Irvine, at the 2021 Google

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Symposium on Foundations of Responsible Computing (FORC) 2022 6/8/2022 Speaker: Shengyuan Hu, Carnegie Mellon ... A Google TechTalk, presented by Yanning Shen, University of California, Irvine, at the 2021 Google Persistent homology provides a way to capture the global structure of data across multiple scales.

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Symposium on Foundations of Responsible Computing (FORC) 2022 6/8/2022 Speaker: Shengyuan Hu, Carnegie Mellon ...
A Google TechTalk, presented by Yanning Shen, University of California, Irvine, at the 2021 Google
Persistent homology provides a way to capture the global structure of data across multiple scales.
A Google TechTalk, presented by Krishna Pillutla, University of Washington, at the 2021 Google

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Reference Gallery

#25 - Jun Wu (UIUC) - Personalized Federated Learning with Parameter Propagation

Dian Shi@UH: Parameterized Knowledge Transfer for Personalized Federated Learning

Personalized Federated Learning

Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity

FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures

Statistical Heterogeneity in Federated Learning

Shengyuan Hu | Private Multi-Task Learning: Formulation and Applications to Federated Learning

CVPR 2026: Domain-Skewed Federated Learning with Feature Decoupling and Calibration

Personalized Graph-Aided Online Federated Model Selection

Junwon You (04/22/26): Persistent Homology as a Lens for Representation Learning

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#25 - Jun Wu (UIUC) - Personalized Federated Learning with Parameter Propagation

#25 - Jun Wu (UIUC) - Personalized Federated Learning with Parameter Propagation

Read more details and related context about #25 - Jun Wu (UIUC) - Personalized Federated Learning with Parameter Propagation.

Dian Shi@UH: Parameterized Knowledge Transfer for Personalized Federated Learning

Dian Shi@UH: Parameterized Knowledge Transfer for Personalized Federated Learning

Read more details and related context about Dian Shi@UH: Parameterized Knowledge Transfer for Personalized Federated Learning.

Personalized Federated Learning

Personalized Federated Learning

Read more details and related context about Personalized Federated Learning.

Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity

Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity

Read more details and related context about Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity.

FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures

FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures

Read more details and related context about FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures.

Statistical Heterogeneity in Federated Learning

Statistical Heterogeneity in Federated Learning

A Google TechTalk, presented by Krishna Pillutla, University of Washington, at the 2021 Google

Shengyuan Hu | Private Multi-Task Learning: Formulation and Applications to Federated Learning

Shengyuan Hu | Private Multi-Task Learning: Formulation and Applications to Federated Learning

Symposium on Foundations of Responsible Computing (FORC) 2022 6/8/2022 Speaker: Shengyuan Hu, Carnegie Mellon ...

CVPR 2026: Domain-Skewed Federated Learning with Feature Decoupling and Calibration

CVPR 2026: Domain-Skewed Federated Learning with Feature Decoupling and Calibration

Read more details and related context about CVPR 2026: Domain-Skewed Federated Learning with Feature Decoupling and Calibration.

Personalized Graph-Aided Online Federated Model Selection

Personalized Graph-Aided Online Federated Model Selection

A Google TechTalk, presented by Yanning Shen, University of California, Irvine, at the 2021 Google

Junwon You (04/22/26): Persistent Homology as a Lens for Representation Learning

Junwon You (04/22/26): Persistent Homology as a Lens for Representation Learning

Persistent homology provides a way to capture the global structure of data across multiple scales. In this talk, I present ...