# Federated Learning on Edge Sensing Devices: A Review

arXiv: https://arxiv.org/abs/2311.01201 · HTML: https://mlsanity.com/paper/2311.01201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 78 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 30 of 34)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Evaluation of On-device Personalization](https://mlsanity.com/paper/1910.10252/) (2019)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://mlsanity.com/paper/1910.01355/) (2019)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [FedCM: Federated Learning with Client-level Momentum](https://mlsanity.com/paper/2106.10874/) (2021)
- [Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications](https://mlsanity.com/paper/1907.01132/) (2019)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [Federated Transfer Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1910.06001/) (2019)
- [Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence](https://mlsanity.com/paper/2007.13018/) (2020)
- [Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data](https://mlsanity.com/paper/2301.09152/) (2023)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison](https://mlsanity.com/paper/2110.10223/) (2021)
- [Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data](https://mlsanity.com/paper/1912.12204/) (2019)

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

- [Federated Learning for Anomaly Detection in Maritime Movement Data](https://mlsanity.com/paper/2512.04635/) (2025)

## Abstract (arXiv)

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studies