# Dispersed Federated Learning: Vision, Taxonomy, and Future Directions

arXiv: https://arxiv.org/abs/2008.05189 · HTML: https://mlsanity.com/paper/2008.05189/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations](https://mlsanity.com/paper/2208.11848/) (2022)

## Abstract (arXiv)

The ongoing deployment of the Internet of Things (IoT)-based smart applications is spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been a significant recent interest in the concept of federated learning. Federated learning offers on-device, privacy-preserving machine learning without the need to transfer end-devices data to a third party location. However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models. Furthermore, the federated learning process might fail due to a failure in the aggregation server (e.g., due to a malicious attack or physical defect). Other than privacy and robustness issues, federated learning over IoT networks requires a significant amount of communication resources for training. To cope with these issues, we propose a novel concept of dispersed federated learning (DFL) that is based on the true decentralization. We opine that DFL will serve as a practical implementation of federated learning for various IoT-based smart applications such as smart industries and intelligent transportation systems. First, the fundamentals of the DFL are presented. Second, a taxonomy is devised with a qualitative analysis of various DFL schemes. Third, a DFL framework for IoT networks is proposed with a matching theory-based solution. Finally, an outlook on future research directions is presented.