Federated stochastic configuration networks for distributed data analytics

Abstract

Stochastic configuration networks (SCNs), as a class of randomized learning models, are incrementally built under a supervisory mechanism, and theoretically ensure error-free learning for training sets. This paper proposes a federated version of SCNs (FSCNs) for large-scale data, which are geographically distributed among different end-user clients with non-shareable data due to privacy and security concerns. Unlike centralized learning that needs to collect data from clients and store them collectively on a cloud server, FSCNs enable distributed analytics in a collaborative learning paradigm without centrally aggregating new data, thereby preventing the leakage of private information. Considering different supervisory and aggregate schemes of model parameters, two FSC algorithms with two aggregate strategies are presented. The experiment results on both data regression and classification show the effectiveness and feasibility of our proposed federated learning scheme.