5.1k citations · 5.9k across the 11 of their papers we have counts for
11 papers
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…
On Tilted Losses in Machine Learning: Theory and Applications
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Exponential tilting is a technique commonly used in fields such as statistics, probability, information theory, and optimization to create parametric distribution shifts. Despite i…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
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…
Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar +1
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in he…
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami +1
Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately a…
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu +5
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can he…
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
Tian Li, Jie Zhong, Ji Liu +2
We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for the…