چكيده به لاتين
Before federated learning (FL), many machine learning algorithms were used to train the model centrally. In other words, all the local data which collected from devices or network nodes and transmitted to a processing center located in the center of the network to implement centralized learning. The mentioned method has several fundamental drawbacks, including limited frequency band and communication power, privacy, data security, and scalability challenges. A novel approach, the FL was suggested to eliminate the aforementioned defects in wireless and mobile telecommunication systems. According to the FL approach, edge devices process their local datasets with regard to the global model announced by a central server (CS) and transmit the results of local learning to it. After aggregating the local updates by the CS, it performs a global update. One of the topics which have been considered in recent years is determining the processing and telecommunication parameters of FL at the edge of the wireless communication networks in order to optimize FL performance. In this research, a multi-antenna base station connected to a unit of a processor, which plays the role of the CS, and a number of Internet of Thing (IoT) devices trying to implement the FL algorithms are considered. Using the simultaneously wireless information and power transfer (SWIPT) technique Inevitably to charge these IoT battery less elements or wirelessly transfer energy to them. The CS transmits the global model in the downlink to the IoT nodes so that they process their local data with regard to the received model from a base station, then the results of processes are sent back to the CS in the uplink. By joint optimization of the telecommunication and processing parameters in such a way that the average implementation time of the FL is minimized and the proper quality of learning is guaranteed. The problem formulated in the third chapter for optimizing the parameters of the considered system is non-convex and includes conditions with mathematical expectation where the probability density function of stochastic variables is unknown. Therefore, to deal with the problem, a two-stage online successive convex approximation method is used, in which, at the first and second stages, the short and long-term parameters are optimized, respectively. Short-term parameters, including processing frequency, downlink and uplink transmission rates, beamforming vectors, power division coefficients, and the local accuracy level parameter of the federated learning algorithm, is also considered as a long-term parameter. In numerical results, it can be observed that the performance loss for the proposed system using the SWIPT is minimal after optimizing the performance of the proposed system compared to a system without energy transfer by using the SWIPT technique, while the proposed system no longer needs a battery or recharging of IoT nodes.
