UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments
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Abstract (arXiv)
Distributed learning and inference algorithms have become indispensable for IoT systems, offering benefits such as workload alleviation, data privacy preservation, and reduced latency. This paper introduces an innovative approach that utilizes unmanned aerial vehicles (UAVs) as a coverage extension relay for IoT environmental monitoring in rural areas. Our method integrates a split learning (SL) strategy between edge devices, a UAV and a server to enhance adaptability and performance of inference mechanisms. By employing UAVs as a relay and by incorporating SL, we address connectivity and resource constraints for applications of learning in IoT in remote settings. Our system model accounts for diverse channel conditions to determine the most suitable transmission strategy for optimal system behaviour. Through simulation analysis, the proposed approach demonstrates its robustness and adaptability, even excelling under adverse channel conditions. Integrating UAV relaying and the SL paradigm offers significant flexibility to the server, enabling adaptive strategies that consider various trade-offs beyond simply minimizing overall inference quality.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 2 Jul 2024.
Builds on
14 of 24 references.
References · 14
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
- 2018Distributed learning of deep neural network over multiple agentscited by 277
- 2017An Introduction to Deep Learning for the Physical Layercited by 227
- 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
- 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
- 2016Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challengescited by 40
- 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
- 2020Communication-Efficient Multimodal Split Learning for mmWave Received Power Predictioncited by 18
- 2021Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networkscited by 7
- 2022LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Datacited by 5
- 2018UAV-Based in-band Integrated Access and Backhaul for 5G Communicationscited by 2
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
- 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoringcited by 4
- 2025Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devicescited by 1
- 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring14 Jul 2025
- 2025Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices12 Feb 2025
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms25 Oct 2024