  The emerging paradigm of federated learning strives to enable collaborative
training of machine learning models on the network edge without centrally
aggregating raw data and hence, improving data privacy. This sharply deviates
from traditional machine learning and necessitates the design of algorithms
robust to various sources of heterogeneity. Specifically, statistical
heterogeneity of data across user devices can severely degrade the performance
of standard federated averaging for traditional machine learning applications
like personalization with deep learning. This paper pro-posesFedPer, a base +
personalization layer approach for federated training of deep feedforward
neural networks, which can combat the ill-effects of statistical heterogeneity.
We demonstrate effectiveness ofFedPerfor non-identical data partitions
ofCIFARdatasetsand on a personalized image aesthetics dataset from Flickr.
