Federated Learning Evolutionary Multi-objective Optimization Algorithm Based on Improved NSGA-III

Federated learning is a novel distributed machine learning method that can train models without sharing raw data.Current federated learning methods suffer from the problem that it is difficult to optimize multiple objectives simultaneously,such as optimizing the model accuracy,optimizing communication costs and balancing the distribution of participants’ performance.A four-objective optimization model and a solution algorithm for federated learning are proposed to address this problem.Data usage cost,global model error rate,model accuracy distribution variance and communication cost are taken as the optimization objectives to construct the optimization model.Aiming at the problem that the solution space of this model is large and the traditional NSGA-III algorithm is difficult to find the optimal solution,the improved NSGA-III federated learning multi-objective optimization algorithm GPNSGA-III based on the Good Point Set initialization strategy is proposed to find the Pareto optimal solution.The algorithm uniformly distributes the limited initialization populations in the objective solution space through the good point set initialization strategy,so that the first-generation solution is maximally close to the optimal value and the ability to find the optimal value is improved compared with the original algorithm.Experimental results show that the hypervolume value of the Pareto solution obtained by the GPNSGA-III algorithm is improved by 107% on average compared with the NSGA-III algorithm;the Spacing value is reduced by 32.3% on average compared with the NSGA-III algorithm;compared with the other multi-objective optimization algorithms,the GPNSGA-III algorithm is more effective in achieving the accuracy of the model while guaranteeing the ba-lance of model accuracy distribution variance,communication cost and data cost.