Fairness and Robustness in Federated Learning with Virginia Smith - #504
Large-scale ML: accuracy, efficiency, fairness
Stanford MLSys Seminar Episode 3: Virginia Smith
CMU - It's Happening Here - Virginia Smith
Minimax Demographic Group Fairness in Federated Learning
Virginia Smith: Evaluating large-scale learning systems
ML Seminar Series - On Heterogeneity in Federated Settings
MedAI #82: Robust and Reliable Federated Learning for Heterogeneous Medical Images | Meirui Jiang
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
Session 7B: FIFL: A Fairness Incentive Framework for Federated Learning
Improving Security and Fairness in Federated Learning Systems
On Heterogeneity in Federated Settings: Workshop on Federated Learning and Analytics Day 2 Keynote
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Session #2
Tilted Losses in Machine Learning: Theory and Applications to Federated Learning, By Tian Li
FedClassAvg: Local representation learning for personalized federated learning on heterogeneous NN
Keynote
Robustness & Personalization in Federated Learning | Dr. Achintya Kundu, IBM Research Singapore
Virginia Smith - A General Framework for Communication-Efficient Distributed... - MLconf SF 2016
Federated and Collaborative Learning with Robustness and Personalization⎟FL workshop
FLOW Seminar #49: Praneeth Karimireddy (EPFL) Practical federated learning
Christoph Lampert: "Fair and Robust Machine Learning I"
Flower Summit 2022 | Demonstrating a Federated Learning Workflow for a Pancreatic Segmentation Model
Fast Convergent Federated Learning
[ICPADS'21] Byzantine-robust Federated Learning through Spatial-temporal Analysis
![[ICPADS'21] Byzantine-robust Federated Learning through Spatial-temporal Analysis](https://img.youtube.com/vi/LQpUme1SJCk/0.jpg)