Securely Distributed Computation with Divided Data and Parameters for Hybrid Particle SwarmOptimization

Machine learning (ML) on cloud and edge systems is widely used to analyze big data and address complex problems. On the other hand, many privacy issues have arisen due to inadequate data security controls. Therefore, studies on secure machine learning on cloud and edge systems have attracted much attention. One of such studies is Federated Learning (FL), in which data is distributed on multiple servers to achieve machine learning through distributed processing. We have proposed a method to realize learning by distributed processing on multiple servers by dividing the learning data and parameters into multiple pieces in advance and distributing these pieces to each server. In previous papers, we have proposed a learning method using local search based on the Steepest Descent Method (SDM) such as Back Propagation (BP) and Neural Gas (NG). In this paper, we propose a learning method that combines a global and local search for solutions for secure distributed processing computation. In particular, we propose a secret distributed processing method for Hybrid Particle Swarm Optimization (HPSO) which is one of the global and local search methods. Its effectiveness is demonstrated by numerical simulations. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.