  Many machine learning (ML) techniques suffer from the drawback that their
output (e.g., a classification decision) is not clearly and intuitively
connected to their input (e.g., an image). To cope with this issue, several
explainable ML techniques have been proposed to, e.g., identify which pixels of
an input image had the strongest influence on its classification. However, in
distributed scenarios, it is often more important to connect decisions with the
information used for the model training and the nodes supplying such
information. To this end, in this paper we focus on federated learning and
present a new methodology, named node liability in federated learning (NL-FL),
which permits to identify the source of the training information that most
contributed to a given decision. After discussing NL-FL's cost in terms of
extra computation, storage, and network latency, we demonstrate its usefulness
in an edge-based scenario. We find that NL-FL is able to swiftly identify
misbehaving nodes and to exclude them from the training process, thereby
improving learning accuracy.
