Confederated Learning: Going Beyond Centralization

Abstract

Traditional machine learning implicitly assumes that a single entity (e.g., a person or an organization) could complete all the jobs of the whole learning process: data collection, algorithm design, parameter selection, and model evaluation. However, many practical scenarios require cooperation among entities, and existing paradigms fail to meet cost, privacy, or security requirements and so on. In this paper, we consider a generalized paradigm: different roles are granted multiple permissions to complete their corresponding jobs, called Confederated Learning. Systematic analysis shows that confederated learning generalizes traditional machine learning and the existing distributed paradigms like federation learning. Then, we study an application scenario of confederated learning which could inspire future research in the context of cooperation between different entities. Three methods are proposed as the first trial for the cooperated learning under restricted conditions. Empirical results on three datasets validate the effectiveness of the proposed methods.