关键词: 联邦边缘学习, 批量梯度下降, 用户调度, 非独立同分布数据

Abstract: Federated edge learning does not need to transmit local data, which greatly reduces the pressure on the uplink while protecting user privacy. The federated edge learning uses the local dataset to train the local model through the intelligent edge device and then uploads the model parameters to the central server; the central server aggregates the local model parameters uploaded locally to form a global model and updates it, and then sends the updated model to the intelligent edge device to start a new iteration. However, the local model accuracy and local model training time will have a significant impact on the global model aggregation and model update process. Therefore, an adaptive dynamic batch gradient descent strategy is firstly proposed, which can automatically adjust the batch size extracted by gradient descent during the local model training process, and optimize the local model accuracy and convergence speed of federated learning. Next, aiming at the non-IID characteristics of user data, an adaptive dynamic batch gradient descent algorithm that combines two-dimensional user scheduling strategies is designed, and two-dimensional constraints are imposed by convergence time and data diversity. After training and testing on the MNIST dataset, fashion MNIST dataset and CIFAR-10 dataset, the algorithm effectively reduces the aggregation waiting time and further improves the global model accuracy and convergence speed. Compared with the gradient descent algorithm with fixed batches of 64, 128, and 256, the global model accuracy of this algorithm is increased by 32.4%, 45.2%, and 87.5% when running for 100 seconds.

Key words: federated edge learning, batch gradient descent, user scheduling, non-independent identically distributed data