  In this paper, we study the problem of recovering the community structure of
a network under federated myopic learning. Under this paradigm, we have several
clients, each of them having a myopic view, i.e., observing a small subgraph of
the network. Each client sends a censored evidence graph to a central server.
We provide an efficient algorithm, which computes a consensus signed weighted
graph from clients evidence, and recovers the underlying network structure in
the central server. We analyze the topological structure conditions of the
network, as well as the signal and noise levels of the clients that allow for
recovery of the network structure. Our analysis shows that exact recovery is
possible and can be achieved in polynomial time. We also provide
information-theoretic limits for the central server to recover the network
structure from any single client evidence. Finally, as a byproduct of our
analysis, we provide a novel Cheeger-type inequality for general signed
weighted graphs.
