Privacy-Preserving Multilayer Community Detection via Federated Learning

Existing frameworks of privacy-preserving multilayer community detection have room for improving detection performance and reducing communication overhead. To address these issues, we propose a novel privacy-preserving multilayer community detection framework based on federated learning which is called federated multilayer community detection (FMCD). First, we propose a novel aggregation strategy by utilizing the network average degree of local networks to aggregate the parameters uploaded by clients in the step of aggregation, which can improve the performance of community detection. Second, we design a training procedure to complete multilayer community detection in multiorganizations, which can reduce communication overhead by transmitting merged community information instead of the global parameter. Finally, experiment results on synthetic and real networks with different criteria illustrate that FMCD can achieve significant performance gains, compared with state-of-the-art algorithms.