Research And Implementation Of Adaptive Training Mechanism For Efficient Federated Learning

With the improvement of computing ability and breakthroughs in artificial intelligence al-gorithms,applications based on neural network models emerge in endlessly,and training these neural network models requires many data.The traditional model training method first transmits the device's data to the cloud data center and then conducts neural network training there.How-ever,the transmission of a large amount of data to the cloud data center will bring transmission pressure to the backbone network,and as people's attention to data privacy gradually rises,data transmission from the device to the cloud data center poses a risk of privacy leakage.Therefore,to solve the shortcomings of traditional neural network model training methods,federated learn-ing sinks the model training process to the device to make full use of the device's private data on the premise of ensuring user privacy.However,compared to model training in cloud data centers,federated learning faces problems: weak computing and communication capabilities,system and data heterogeneity.These problems reduce the efficiency of federated learning and bring challenges to constructing a high-efficiency federated learning training framework.In response to the above problems,existing work has researched how to improve the effi-ciency of federated learning and has made some progress,but there are still some problems.On the one hand,the current related work assumes that all devices perform the same training round.However,due to the heterogeneity of system resources and the different amount of data,the generation of stragglers reduces the efficiency of federated learning and training.On the other hand,the current related work ignores the influence of the synchronization frequency between the device and the cloud data center on the accuracy of the model convergence.Although the use of low-frequency synchronization can improve the efficiency of federated learning,it will also reduce the accuracy of model convergence.In this regard,this paper studies the adaptive allocation mechanism for the number of train-ing iterations and the adaptive adjustment mechanism for synchronous frequency to improve the efficiency of federated learning.To solve the problem that stragglers reduce the efficiency of federated learning,this paper analyzes the influence of training iterations on the efficiency of federated learning.Then an adaptive allocation mechanism for the number of training iterations is proposed,which allocates an appropriate number of training iterations to the device accord-ing to the system resources and data distribution.The mechanism solves system heterogeneity and data heterogeneity faced by federated learning and effectively improves the efficiency of federated learning.To solve the problem that the synchronization frequency affects the accu-racy of model convergence,this paper analyzes the effect of synchronization frequency on the efficiency of training and model convergence accuracy.Then a federated learning adaptive ad-justment mechanism for synchronous frequency is proposed,which can dynamically adjust the synchronization frequency according to the convergence state of the global model.Thereby,the contradiction between training efficiency and model convergence accuracy can be resolved,so that federated learning can improve the efficiency of federated learning training while guaran-teeing the accuracy of model convergence.Finally,based on the theoretical research results,this paper designs and implements a federated learning adaptive training system,and conducts comparative experiments based on this system to verify the effectiveness of the two adaptive mechanisms.In summary,to improve the efficiency of federated learning,the adaptive allocation mech-anism for the number of training iterations and the adaptive adjustment mechanism for syn-chronous frequency are proposed in this paper.And a federated learning adaptive training sys-tem is constructed in this paper.Experiments show that these training mechanisms can improve the efficiency of federated learning while ensuring the accuracy of model convergence.These mechanisms and the system proposed in this paper are helpful to build a federated learning ecosystem,which can be applied to federated learning business scenarios.