5 research outputs found
Robustness Analytics to Data Heterogeneity in Edge Computing
Federated Learning is a framework that jointly trains a model \textit{with}
complete knowledge on a remotely placed centralized server, but
\textit{without} the requirement of accessing the data stored in distributed
machines. Some work assumes that the data generated from edge devices are
identically and independently sampled from a common population distribution.
However, such ideal sampling may not be realistic in many contexts. Also,
models based on intrinsic agency, such as active sampling schemes, may lead to
highly biased sampling. So an imminent question is how robust Federated
Learning is to biased sampling? In this
work\footnote{\url{https://github.com/jiaqian/robustness_of_FL}}, we
experimentally investigate two such scenarios. First, we study a centralized
classifier aggregated from a collection of local classifiers trained with data
having categorical heterogeneity. Second, we study a classifier aggregated from
a collection of local classifiers trained by data through active sampling at
the edge. We present evidence in both scenarios that Federated Learning is
robust to data heterogeneity when local training iterations and communication
frequency are appropriately chosen