Federated feature reconstruction with collaborative star networks

Abstract

Federal learning provides a secure platform for sharing sensitive data, yet imposes stringent requirements on the data. Non-IID data often cannot fully enjoy the convenience it offers. When clients possess divergent feature sets, retaining only the common features is a prevalent yet suboptimal practice. This paper proposes a novel omnidirectional federated learning framework that employs a Star collaboration network designed to leverage independent information from client nodes for feature reconstruction of other clients. It establishes an approximate distribution network, reinforcing feature correlations while overcoming data isolation seen in traditional federal learning. Additionally, homomorphic encryption is utilized to ensure data security throughout the transmission process. Experimental evaluations on structured datasets demonstrate that the reconstructed prediction results closely approximate those under the condition of complete data, confirming the effectiveness of the Star network in data completion and multi-party prediction scenarios.