Sequential knowledge editing techniques aim to continuously update knowledge in large language models at low cost, preventing models from generating outdated or incorrect information. However, existing sequential editing methods suffer from a significant decline in editing success rates after long-term editing. Through theoretical analysis and experiments, our findings reveal that as the number of edits increases, the model's output increasingly deviates from the desired target, leading to a drop in editing success rates. We refer to this issue as the superimposed noise accumulation problem. Our further analysis demonstrates that the problem is related to the erroneous activation of irrelevant knowledge and conflicts between activated knowledge. Based on this analysis, a method named DeltaEdit is proposed that reduces conflicts between knowledge through dynamic orthogonal constraint strategies. Experiments show that DeltaEdit significantly reduces superimposed noise, achieving a 16.8% improvement in editing performance over the strongest baseline.

Empowered by the huge amounts of sensor data in Industrial Internet of Things (IIOT), deep learning models have made remarkable achievements in the field of rotating machinery fault diagnosis. To improve the diagnosis performance under unknown working conditions, domain generalization technologies have been extensively studied. However, the existing methods predominantly gather the sensor data from multiple source domains together for model training, which poses a threat to data privacy in the IIOT. To address this problem, this paper proposes a novel gradient alignment federated domain generalization (GAFedDG) framework for rotating machinery fault diagnosis. In the proposed GAFedDG, an intra-domain gradient aligning mechanism is designed to minimize the gradient discrepancy between the current classifier on raw signals and augmented signals, effectively preventing the local model from overfitting the domain-specific fault knowledge. In addition, to bridge the domain shifts across multiple scattered source domains, an inter-domain gradient aligning mechanism is implemented to minimize the gradient discrepancy between the current classifier and other domain classifiers. By combining the two mechanisms above, a domain-agnostic model that can generalize well on unseen working conditions is established. Extensive experimental results on two self-built test rigs show that the GAFedDG possesses superior generalization capability in privacy-preserving scenarios.