Adaptive Graph-Based Framework for Secure and Efficient Distributed Data Processing
Keywords:
Adaptive Graph Processing, Distributed Data Processing, Graph-Based Framework, Secure Data Computation, Dynamic Graph Partitioning, Privacy Preserving Data Analytics, Scalable Distributed Systems, Graph Neural Networks, Fault-Tolerant Computing, Resource-Aware Optimization.Abstract
Distributed data processing enables computationby distributing workload among multiple workers. By outsourcing data operations to distributed infrastructures suchas Edge and Cloud, users enjoy advantages in privacy,
References
[1] 1. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. DOI
[2] 2. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81. DOI
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Published
2021-06-15
How to Cite
Sasi Kumar Kolla. (2021). Adaptive Graph-Based Framework for Secure and Efficient Distributed Data Processing. Journal of Computational Analysis and Applications (JoCAAA), 29(6), 1519–1523. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5860
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