In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogeneous data. These data forms a stream which requires special handling. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preservation of both privacy and efficiency. Federated learning emerged as a distributed learning method that trains models locally and aggregates them on a server to preserve data privacy. This paper showcases two illustrative scenarios that highlight the potential of federated learning (FL) as a key to delivering efficient and privacy-preserving machine learning within IoT networks. We first give the mathematical foundations for key aggregation algorithms in federated learning, i.e., FedAvg and FedProx. Then, we conduct simulations, using Flower Framework, to show the \textit{efficiency} of these algorithms by training deep neural networks on common datasets and show a comparison between the accuracy and loss metrics of FedAvg and FedProx. Then, we present the results highlighting the trade-off between maintaining privacy versus accuracy via simulations - involving the implementation of the differential privacy (DP) method - in Pytorch and Opacus ML frameworks on common FL datasets and data distributions for both FedAvg and FedProx strategies.

物联网网络和数据处理技术的发展对分布式学习方法中的联邦学习具有巨大潜力，本论文通过两个实例展示了联邦学习在物联网网络中实现高效和保护隐私的机器学习的潜力，并通过模拟研究了隐私和准确性之间的权衡关系。

联邦学习算法的效率和隐私的实证研究