We consider learning from data of variable quality that may be obtained from
different heterogeneous sources. Addressing learning from heterogeneous data in
its full generality is a challenging problem. In this paper, we adopt instead a
model in which data is observed through heterogeneous noise, where the noise
level reflects the quality of the data source. We study how to use stochastic
gradient algorithms to learn in this model. Our study is motivated by two
concrete examples where this problem arises naturally: learning with local
differential privacy based on data from multiple sources with different privacy
requirements, and learning from data with labels of variable quality.
The main contribution of this paper is to identify how heterogeneous noise
impacts performance. We show that given two datasets with heterogeneous noise,
the order in which to use them in standard SGD depends on the learning rate. We
propose a method for changing the learning rate as a function of the
heterogeneity, and prove new regret bounds for our method in two cases of
interest. Experiments on real data show that our method performs better than
using a single learning rate and using only the less noisy of the two datasets
when the noise level is low to moderate
