Split Federated Learning for Real-Time Aerial Video Event Recognition in UAV-Based Geospatial Monitoring

Document Type

Article

Source of Publication

IEEE Transactions on Geoscience and Remote Sensing

Publication Date

1-1-2025

Abstract

Event recognition in UAV-based monitoring systems is crucial for geospatial analysis, environmental surveillance, and disaster response. However, federated learning (FL)-based approaches for real-time event recognition face significant challenges, including spatiotemporal heterogeneity, constrained computational resources, and communication limitations in distributed UAV networks. Although FL enables decentralized machine learning with enhanced data privacy, its application to temporal event recognition in UAV-based remote sensing systems requires addressing data heterogeneity and maintaining spatiotemporal coherence across distributed nodes. To address these challenges, we propose a Split Federated Learning (SFL) framework tailored for UAV-assisted event recognition in geospatial monitoring. The proposed SFL architecture partitions computational workloads between UAV-based edge clients and a central server, optimizing both on-device efficiency and global model performance. At the client level, lightweight convolutional models extract spatiotemporal features from UAV-captured video sequences, reducing computational complexity and transmission overhead. These extracted features are transmitted to a central server, where higher-order temporal dependencies are learned, enabling robust event classification. To enhance adaptability in dynamic environments, we integrate dynamic video chunking, adaptive temporal pooling, and modality-agnostic feature aggregation, ensuring efficient processing of variable-length sequences while minimizing bandwidth constraints. Experimental evaluations on standard UAV-based geospatial datasets demonstrate that the proposed SFL framework significantly outperforms conventional FL approaches in terms of classification accuracy, communication efficiency, and scalability. This work provides a scalable, privacy-preserving, and computationally efficient solution for real-time temporal event recognition in UAV-assisted geospatial monitoring applications.

ISSN

0196-2892

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Disciplines

Computer Sciences

Keywords

Event Recognition, Real-Time Processing, Spatiotemporal Learning, Split Federated Learning, UAV-Based Monitoring

Scopus ID

105013060476

Indexed in Scopus

yes

Open Access

no

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