An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms

TL;DR

This paper empirically evaluates federated learning algorithms, FedAvg and FedProx, focusing on their efficiency and privacy trade-offs in IoT networks through simulations and mathematical analysis.

Contribution

It provides a mathematical foundation for key aggregation algorithms and compares their efficiency and privacy-preserving capabilities through extensive simulations.

Findings

FedAvg and FedProx show different trade-offs between accuracy and privacy.

Simulations demonstrate the efficiency of FL algorithms in training deep neural networks.

Differential privacy impacts the accuracy of federated learning models.

Abstract

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.