Quick and accurate emergency handling in Disaster Decision Support Systems (DDSS) is often hampered by network latency and suboptimal application accuracy. While Federated Learning (FL) addresses some of these issues, it is constrained by high communication costs and rigid synchronization requirements across heterogeneous convolutional neural network (CNN) architectures. To overcome these challenges, this paper proposes a decentralized ensembling framework based on asynchronous probability aggregation and feedback distillation. By shifting the exchange unit from model weights to class-probability vectors, our method maintains data privacy, reduces communication requirements by orders of magnitude, and improves overall accuracy. This approach enables diverse CNN designs to collaborate asynchronously, enhancing disaster image identification performance even in resource-constrained settings. Experimental tests demonstrate that the proposed method outperforms traditional individual backbones and standard federated approaches, establishing a scalable and resource-aware solution for real-time disaster response.
@article{arxiv.2604.14450,
title = {Asynchronous Probability Ensembling for Federated Disaster Detection},
author = {Emanuel Teixeira Martins and Rodrigo Moreira and Larissa Ferreira Rodrigues Moreira and Rodolfo S. Villaça and Augusto Neto and Flávio de Oliveira Silva},
journal= {arXiv preprint arXiv:2604.14450},
year = {2026}
}
Comments
Paper accepted for publication at 31st IEEE Symposium on Computers and Communications (ISCC) 2026