Federated Learning (FL) is a distributed machine learning technique that
allows model training among multiple devices or organizations without sharing
data. However, while FL ensures that the raw data is not directly accessible to
external adversaries, adversaries can still obtain some statistical information
about the data through differential attacks. Differential Privacy (DP) has been
proposed, which adds noise to the model or gradients to prevent adversaries
from inferring private information from the transmitted parameters. We
reconsider the framework of differential privacy federated learning in
resource-constrained scenarios (privacy budget and communication resources). We
analyze the convergence of federated learning with differential privacy (DPFL)
on resource-constrained scenarios and propose an Adaptive Local Steps
Differential Privacy Federated Learning (ALS-DPFL) algorithm. We experiment our
algorithm on the FashionMNIST and Cifar-10 datasets and achieve quite good
performance relative to previous work.

在资源受限的情况下重新考虑差分隐私联邦学习的框架，并分析其在收敛性方面的情况，提出了自适应本地步差分隐私联邦学习 (ALS-DPFL) 算法。通过在 FashionMNIST 和 CIFAR-10 数据集上的实验，相对于以往的工作，我们取得了相当良好的性能。