Sustainable collaboration: Federated learning for environmentally conscious forest fire classification in Green Internet of Things (IoT)

Siddique, Ali Akbar; Alasbali, Nada; Driss, Maha; Boulila, Wadii; Alshehri, Mohammed S.; Ahmad, Jawad

doi:10.1016/j.iot.2023.101013

Cited by 6 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Siddique et al introduced a novel framework utilizing Federated Stochastic Gradient Descent (FedSGD) and Internet of Things (IoT) technology to improve fire detection. They applied this method to a Kaggle fire dataset distinguishing Fire and No-Fire classes, achieving 99.27 % accuracy [ 4 ]. Concurrently, Yar et al utilized vision transformers across four datasets: Foggia's (31 videos, 14 with fire scenes), FD (benchmarking Foggia's and BoWFire datasets with fire and normal categories), Yar (addressing fire-like colors with 1000 forest-fire and 1000 non-fire images), and a complex fire dataset (7642 images capturing various fire scenarios, including 4036 fire incidents).…”

Section: Methodsmentioning

confidence: 99%

“…However, obtaining real-time fire datasets presents a significant challenge due to the complexities of creating suitable experimental conditions [ 3 ]. The urgency of forest fires, with their potential for rapid spread, emphasizes the importance of timely detection mechanisms [ 4 ]. Existing literature indicates a scarcity of readily available real-time UAV datasets, primarily obtained from online repositories, still photos and processed using software tools such as Photoshop [ [5] , [6] , [7] ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs)

Mowla,

Asadi,

Tekeoglu

et al. 2024

Data in Brief

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs)

Mowla,

Asadi,

Tekeoglu

et al. 2024

Data in Brief

View full text Add to dashboard Cite

“…The deployment of an intrusion detection system becomes imperative for identifying and distinguishing between normal and malicious traffic on the network 14 – 16 . Compared to other learning techniques, a federated learning-based detection system significantly improves overall accuracy 17 – 19 .…”

Section: Motivationmentioning

confidence: 99%

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Babbar,

Rani,

Boulila

2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users’ private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.

show abstract

Towards sustainable industry 4.0: A survey on greening IoE in 6G networks

Alsamhi,

Hawbani,

Sahal

et al. 2024

Ad Hoc Networks

View full text Add to dashboard Cite

Sustainable collaboration: Federated learning for environmentally conscious forest fire classification in Green Internet of Things (IoT)

Cited by 6 publications

References 29 publications

UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs)

UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs)

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Towards sustainable industry 4.0: A survey on greening IoE in 6G networks

Contact Info

Product

Resources

About