5.1k citations · 5.9k across the 11 of their papers we have counts for
4 papers · 1 filter
Differentially Private Adaptive Optimization with Delayed Preconditioners
Tian Li, Manzil Zaheer, Ken Ziyu Liu +3
Privacy noise may negate the benefits of using adaptive optimizers in differentially private model training. Prior works typically address this issue by using auxiliary information…
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Shanshan Wu, Tian Li, Zachary Charles +4
Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metr…
Maximizing Global Model Appeal in Federated Learning
Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li +2
Federated learning typically considers collaboratively training a global model using local data at edge clients. Clients may have their own individual requirements, such as having…
Private Adaptive Optimization with Side Information
Tian Li, Manzil Zaheer, Sashank J. Reddi +1
Adaptive optimization methods have become the default solvers for many machine learning tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential…