Forest Fire Image Classification Through Decentralized DNN Inference
Year: 2024
Type: conference-paper
Abstract: In the realm of Natural Disaster Management (NDM), timely communication with local authorities is paramount for an effective response. To achieve this, multi-agent systems play a pivotal role by proficiently identifying and categorizing various disasters. In the field of Distributed Deep Neural Network (D-DNN) inference, such approaches often require DNN nodes to transmit their results to the cloud for inference, or they necessitate the establishment of a fixed topology network to enable inference directly on the edge, a practice prone to security risks. In this work, we propose a decentralized inference strategy tailored for fire classification tasks. In this approach, individual DNN nodes communicate within a network and enhance their predictions by considering other DNN node inference outputs that contribute to improving their individual performance. The overall coordination of the system on a specific decision is achieved through a consensus protocol, which acts as a universally accepted inference rule adopted by all DNN nodes operating within the system. We present a comprehensive experimental analysis, of the forest-fire classification task, focusing on enhancing both individual DNN node performance and the stability of the consensus protocol.
Authors: Dimitrios Papaioannou, Vasileios Mygdalis, Ioannis Pitas
Institutions: Aristotle University of Thessaloniki, University of Antwerp
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404915062