5.1k citations · 7.5k across the 42 of their papers we have counts for
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Ditto: Fair and Robust Federated Learning Through Personalization
Tian Li, Shengyuan Hu, Ahmad Beirami +1
Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measu…
Is Support Set Diversity Necessary for Meta-Learning?
Amrith Setlur, Oscar Li, Virginia Smith
Meta-learning is a popular framework for learning with limited data in which an algorithm is produced by training over multiple few-shot learning tasks. For classification problems…
Tilted Empirical Risk Minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or tr…
FedDANE: A Federated Newton-Type Method
Tian Li, Anit Kumar Sahu, Manzil Zaheer +3
Federated learning aims to jointly learn statistical models over massively distributed remote devices. In this work, we propose FedDANE, an optimization method that we adapt from D…