  Federated Learning (FL) is a novel distributed machine learning approach to
leverage data from Internet of Things (IoT) devices while maintaining data
privacy. However, the current FL algorithms face the challenges of
non-independent and identically distributed (non-IID) data, which causes high
communication costs and model accuracy declines. To address the statistical
imbalances in FL, we propose a clustered data sharing framework which spares
the partial data from cluster heads to credible associates through
device-to-device (D2D) communication. Moreover, aiming at diluting the data
skew on nodes, we formulate the joint clustering and data sharing problem based
on the privacy-preserving constrained graph. To tackle the serious coupling of
decisions on the graph, we devise a distribution-based adaptive clustering
algorithm (DACA) basing on three deductive cluster-forming conditions, which
ensures the maximum yield of data sharing. The experiments show that the
proposed framework facilitates FL on non-IID datasets with better convergence
and model accuracy under a limited communication environment.
