Research On Optimization Methods For Federated Learning Model Training

With the rapid development of mobile Internet,edge computing,and IoT technologies,the smart terminal devices accessed in the network generate massive data,which contain a large amount of sensitive information,and these data are at serious risk of leakage during the transmission process.Federated learning technology,as an emerging computing paradigm,provides a strong support to realize the mining and analysis of large-scale distributed data under the premise of safeguarding user privacy.However,due to the differences in resources and task requirements between devices,the difficulty of obtaining training data labels,and the non-independent and identically distributed data,there are still many challenges in using existing federated learning methods to achieve collaborative model training.Therefore,it is an urgent and challenging problem to build an efficient federated learning model training optimization method to meet the objectivity requirements in practical application scenarios such as IoT.To address the above challenges,this paper systematically investigates the key techniques of federated learning model training optimization methods for the correlation and label structure characteristics of device data,aiming to effectively utilize the computing power of edge nodes to achieve efficient model sharing without leaving the local area,and study different training strategies for different application scenarios.The specific research contents and contributions are as follows:(1)Explore collaborative optimization methods for federated learning models in fully supervised scenarios.This paper focuses on the problem of low efficiency in collaborative training of existing federated learning methods caused by imbalanced client resources.The research in this paper investigates a federated learning collaborative training method based on data correlation.Inspired by the resource scheduling mechanism in edge intelligence,the cloud-edgeclient federated learning architecture is designed to reduce the communication overhead through the data selection strategy of Bayesian convolutional neural network,and the asynchronous parameter aggregation algorithm is proposed to improve the training efficiency of the system.Subsequently,multi-task learning algorithms based on sparse shared networks are investigated,iterative pruning networks are designed to build sparse shared architectures,and task routing networks are proposed to design independent task sub-networks for different nodes.In addition,an adaptive loss function is designed to dynamically adjust the priority among tasks.Detailed theoretical analysis and experimental results show that the method designed in this paper can fuse a large number of tasks from different devices into a shared model,which improves the scalability of the system while ensuring the accuracy of the system model.(2)To address the challenge of model optimization due to insufficient labeled data in federated training,this paper investigates a knowledge transferbased federated semi-supervised model optimization method from two perspectives:inter-domain knowledge transfer and inter-node knowledge transfer.Firstly,we propose a domain fusion-based federated transfer learning algorithm that achieves semi-supervised model training through domain fusion.An adaptive incremental layer is introduced to alleviate the overfitting issue of the model to labeled data.Next,we study a semi-supervised learning mechanism based on generative adversarial networks.We design a mechanism for semi-supervised model training that facilitates knowledge transfer between clients.Additionally,a dynamic aggregation mechanism is proposed to adaptively adjust the weights of each client during the parameter aggregation process.Detailed theoretical analysis and experimental results demonstrate that the proposed methods in this paper achieve high accuracy and scalability,effectively meeting the requirements of semi-supervised collaborative optimization of models in practical applications.(3)Building upon the foundation of federated collaborative tasks with limited training data labels,this paper further addresses the issue of the inability to perform collaborative training in federated learning systems when no labeled data is available.From the perspective of model training in unsupervised scenarios,we re-examine the image representation extraction in the federated learning training process and investigate a federated unsupervised model training method based on representation learning.Firstly,we study model collaborative training in scenarios where no labeled data is available at the clientside.We propose a federated contrastive learning algorithm based on knowledge distillation.Specifically,contrastive learning methods are employed for distributed pre-training of local models at each client.Parameter aggregation and model fine-tuning are performed at the cloud server with limited labeled data,and unsupervised knowledge distillation is conducted across all clients to preserve personalized local models.Additionally,an unsupervised clustering algorithm based on feature similarity is designed.Starting from the scenario where neither the cloud nor the clients have access to labeled data during the training process,an unsupervised clustering module is designed.To address the difficulty of model convergence caused by non-independent and identically distributed data,a dynamic updating mechanism is devised to determine how to update the local models based on the differences in model parameters.E xperiments demonstrate that the aforementioned improvements greatly benefit the federated unsupervised learning task in terms of generalization,highlighting the effectiveness of federated unsupervised learning as a collaborative representation learning framework.