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

摘要： 联邦边缘学习无需传输本地数据，在保护用户隐私的基础上，大大降低了上行链路压力。联邦边缘学习通过智能边缘设备，利用本地数据集训练局部模型后上传模型参数至中心服务器；中心服务器聚合本地上传的局部模型参数形成全局模型后进行更新，然后将更新后的模型下发给智能边缘设备开始新一轮迭代。但是局部模型精度以及局部模型训练时间，对全局模型聚合以及模型更新过程会产生重大影响。因此提出自适应动态批量梯度下降策略，在局部模型训练过程中自动调整梯度下降抽取的批量大小，优化联邦学习的局部模型精度及收敛速度。针对用户数据的非独立同分布特性，设计一种联合双维度用户调度策略的自适应动态批量梯度下降算法，通过收敛时间和数据多样性进行双维度约束。经MNIST数据集、fashion MNIST数据集和CIFAR-10数据集的训练测试，算法在有效降低聚合等待时间的同时能够进一步提高全局模型精度和收敛速度。与固定批量为64、128、256的梯度下降算法相比，该算法的全局模型精度在运行100 s时提升分别为32.4%、45.2%、87.5%。

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