Now-a-days, many high-end applications are turning to grid computing to meet their computational and data storage needs. High-end applications require a wide variety of computational resources as well as long time to ...
详细信息
Now-a-days, many high-end applications are turning to grid computing to meet their computational and data storage needs. High-end applications require a wide variety of computational resources as well as long time to produce the desired output. These resources should be utilized efficiently and effectively for overall performance improvement. In this paper, we present an efficient resource management architecture in grid environment using grid services. The basic technique is to monitor certain values of some parameters of grid services which provide imprecise or partial state information of the services during execution time and depending on the condition values of these parameters (specified earlier at job submission), services can be stopped at any time. Thus our architecture can save a great amount of computing resources as well as time from being wasted to produce wrong output and improves overall performance by utilizing available resources to run other services. Our proposed framework can also cope with the real- time applications. Experiment result shows that our architecture efficiently manages the computing resources and significantly saves valuable time.
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial...
详细信息
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.
暂无评论