An Empirical Study of Efficiency and Privacy of Federated Learning
  Algorithms
Sofia Zahri Hajar Bennouri Ahmed M. Abdelmoniem
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.