Knowledge sharing among nodes improves incident detection accuracy in decentralized systems by enabling collaboration and information exchange between different local datasets within a networked graph structure. By leveraging distributed optimization frameworks like Network Lasso, each node can contribute its unique insights to the collective learning process, leading to more robust and accurate incident detection models.
Through knowledge sharing, nodes can learn from similar context environments represented by neighboring nodes, enhancing their ability to distinguish actual incidents from normal patterns accurately. This collaborative approach fosters better generalization across diverse traffic regions and mitigates issues related to imbalanced datasets or sparse incidents occurrences.
Overall, knowledge sharing among nodes promotes synergy between individual detectors within a decentralized system, resulting in improved overall performance and reliability in detecting traffic incidents effectively.