5.1k citations · 9.6k across the 102 of their papers we have counts for
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Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer +3
Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms…
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…
A System for Massively Parallel Hyperparameter Tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh +4
Modern learning models are characterized by large hyperparameter spaces and long training times. These properties, coupled with the rise of parallel computing and the growing deman…
Model Agnostic Supervised Local Explanations
Gregory Plumb, Denali Molitor, Ameet Talwalkar
Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and…