Sürdürülebilir Kalkınma Amaçları
Yazarlar
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ2Federated agricultural yield forecasting under an Edge–Fog–Cloud framework: a multi-country privacy-preserving approach
Utku, Anıl; Barut, Abdulkadir; Alofaysan, Hind; Henni, Mohamed Djafar; Amoozegar, Azadeh
Predicting agricultural yields is crucial for ensuring global food security and adaptation to climate change. However, the centralized collection of agricultural data across countries might pose governance, privacy, and data sovereignty issues. This research presents a privacy-preserving model for predicting agricultural yields using federated learning within an edge-fog-cloud integrated architecture. The experiments have been performed on a European cereal production dataset for the period 1990–2022, using a time-aware forecasting scheme. Centralized, edge-only, federated, and hybrid learning techniques have been systematically compared to examine the influence of distributed optimization on forecasting performance. It can be seen that central classical machine learning algorithms have yielded the best predictive performance. Federated deep learning models, meanwhile, showed relatively poor forecasting accuracy compared to their centralized counterparts under a non-IID distribution of multi-country agricultural data. Edge-only learning, meanwhile, has yielded comparatively low generalization capabilities due to the absence of collaborative learning.
- MakaleYalnız künye2026Scopus SJRQ1FedMedIDS: A non-IID-resilient federated DCNN framework for securing IoMT and IoHT devices
Gencer, Burak; Dogan, Gurkan
The diversity of devices in the Internet of Medical Things (IoMT) and Internet of Health Things (IoHT) networks creates significant security vulnerabilities against cyberattacks. Traditional centralized machine learning approaches, however, are not always suitable for these environments due to data privacy regulations. In this study, a new Deep Convolutional Neural Network (DCNN) architecture is proposed for attack detection in IoMT/IoHT networks. The proposed architecture integrates the Residual Squeeze-and-Excitation (RSE) Block, the Squeeze-and-Excitation attention mechanism (SEM), and a one-dimensional multi-pooling approach. The model was evaluated within a federated learning (FL) framework called Federated Medical Intrusion Detection System (FedMedIDS), which preserves data privacy and demonstrates robustness in heterogeneous client scenarios. Four different training strategies—Centralized, FedAvg, FedProx, and Local-Only—were adopted for the evaluations. The proposed model and existing models were tested on the Canadian Institute for Cybersecurity – Internet of Medical Things 2024 (CICIoMT2024) and Edith Cowan University – Internet of Health Things (ECU-IoHT) datasets and compared with current deep learning models. The DCNN proposed for the CICIoMT2024 dataset and a Dirichlet distribution with α = 1.0 achieved F1 scores of up to 99.92% under FedAvg and 94.64% under FedProx with α = 0.5. At α = 0.05, the highest level of heterogeneity, it outperformed the Long Short-Term Memory (LSTM) by as much as 27%. On the ECU-IoHT dataset, F1 scores of up to 89.57% under FedAvg and 89.50% under FedProx were achieved at α = 1.0. This represents an improvement of up to 37% over LSTM at the same level of heterogeneity.
