# Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning

arXiv: https://arxiv.org/abs/2306.02990 · HTML: https://mlsanity.com/paper/2306.02990/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [Quantized Federated Learning under Transmission Delay and Outage Constraints](https://mlsanity.com/paper/2106.09397/) (2021)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms](https://mlsanity.com/paper/2007.12004/) (2020)
- [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020)
- [Survey of Important Issues in UAV Communication Networks](https://mlsanity.com/paper/1603.08462/) (2016)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles](https://mlsanity.com/paper/2007.06378/) (2020)
- [Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration](https://mlsanity.com/paper/2206.05949/) (2022)
- [A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1801.01656/) (2018)
- [Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model](https://mlsanity.com/paper/2104.10378/) (2021)
- [Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks](https://mlsanity.com/paper/2101.12472/) (2021)

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

- [Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networks](https://mlsanity.com/paper/2508.12079/) (2025)
- [Joint Hybrid Beamforming and Artificial Noise Design for Secure Multi-UAV ISAC Networks](https://mlsanity.com/paper/2509.23687/) (2025)
- [Personalized Federated Learning by Energy-Efficient UAV Communications](https://mlsanity.com/paper/2605.25212/) (2026)

## Abstract (arXiv)

Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately.