Edge-Based Predictive Data Reduction for Smart Agriculture: A Lightweight Approach to Efficient IoT Communication

Hi-SAFE, a new framework for Hierarchical Secure Aggregation in Federated Learning (FL), addresses privacy and communication efficiency challenges in resource-constrained environments like IoT and edge networks. It enhances the security of sign-based methods, such as SIGNSGD-MV, by utilizing efficient majority vote polynomials derived from Fermat's Little Theorem.

A new study has introduced a problem-oriented taxonomy of evaluation metrics for time series anomaly detection, addressing the challenges posed by diverse application objectives and varying metric assumptions in IoT and cyber-physical systems. The framework categorizes over twenty metrics into six dimensions, focusing on their specific evaluation challenges rather than their mathematical forms. Comprehensive experiments were conducted to assess metric behavior across different detection scenarios.

A novel framework has been proposed for detecting and sanitizing data poisoning in human activity recognition (HAR) systems within wearable IoT ecosystems, leveraging large language models (LLMs) to enhance adaptability and robustness against attacks. This approach utilizes zero-shot, one-shot, and few-shot learning paradigms, incorporating role play prompting to simulate expert responses.