| As the number of Io T devices continues to grow,huge amounts of data are being generated,providing huge amounts of raw materials for machine learning,yet there are many problems to be addressed in the utilization of this data.In the traditional centralized machine learning model,all data are collected in a central node for processing,which not only has the risk of privacy leakage,but also requires a large amount of centralized computing resources.In order to solve the problems caused by traditional centralized machine learning,federation learning has emerged as an emerging machine learning technique that can utilize the computing power of end devices and local data for model training while protecting personal privacy.However,federation learning also has some problems because of the heterogeneity of the device itself and the network environment,especially when a large number of devices are involved,existing federation learning algorithms are prone to high communication costs,such as the need for great uplink bandwidth with low communication latency.To address this bottleneck,this thesis proposes a communication-efficient federation learning method called CE-Fed PA.The method consists of two parts: data compression,and device selection.In the data compression part,the Adam optimization algorithm is introduced to avoid the loss of model information and the reduction of convergence speed after data compression.The parameter matrix,first-order moment matrix,and second-order moment matrix generated by the Adam optimization algorithm are sparse,quantized,and Rice coded,and the convergence analysis of the quantized federal optimization is done.The subsequent quantitative analysis results show that the compression rate can reach 2-15 times for different sparse rates compared to the traditional federation learning method,and 6-45 times for different sparse rates compared to the traditional federation learning method using the Adam optimization algorithm,thus reducing the amount of data uploaded by the client.In the device selection strategy section,the set of devices is dynamically selected using the device contribution effect factor,a metric based on the one proposed in this thesis,as the priority.The device contribution effect factor is influenced by the device data quality and computing power.Then,a multi-constrained linear programming mathematical model of the device contribution effect is proposed,and the problem is solved by a dynamic programming algorithm,and finally,the correctness and effectiveness of the algorithm are analyzed,and the device selection strategy avoids the participation of of devices with poor communication network quality,thus reducing the communication consumption.In order to verify the communication efficiency of this system,both independent and non-independent and identically distributed are explored in experiments on publicly available datasets commonly used in the field of artificial intelligence,and extensive experiments are conducted using both simulation and real-world environments.The results of simulation experiments show that the CE-Fed PA algorithm proposed in this thesis performs better than the traditional federation learning algorithm in terms of communication efficiency,requiring 1.8 times and 5.8 times less communication time to achieve the same model accuracy,respectively.In the real-world experiment,the communication time of this system is also reduced by 2.2 times in an end-to-end real Io T environment consisting of four Android phones and one computer as a server.The results show that the system can greatly reduce the amount of uploaded data and reduce the communication delay under the premise that the accuracy of the model is basically not affected. |
