2 papers
cs.LG2026
Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation
Kenechi Omeke, Michael Mollel, Lei Zhang +2
Anomaly detection is a core service in the Internet of Underwater Things, yet training accurate distributed models underwater is difficult because acoustic links are low-bandwidth,…
eess.SY2026
Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation
Kenechi Omeke, Attai Abubakar, Michael Mollel +3
The Internet of Underwater Things (IoUT) is becoming a critical infrastructure for ocean observation, marine resource management, and climate science. Its development is hindered b…