# An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms

arXiv: https://arxiv.org/abs/2312.15375 · HTML: https://mlsanity.com/paper/2312.15375/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 24 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data](https://mlsanity.com/paper/2105.07066/) (2021)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks](https://mlsanity.com/paper/2102.07477/) (2021)

## Built on by (most cited first, 1 of 1)

- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)

## Abstract (arXiv)

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 textitefficiency 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.