Dissecting Failure Patterns in Neural Network Testing under Adversarial Scenarios

Buana Monang

Dissecting Failure Patterns in Neural Network Testing under Adversarial Scenarios

Authors

Buana Monang

Data Architects, Indonesia.

Keywords:

Adaptive Machine Learning, Distributed Computing, Scalable Decision Making, Reinforcement Learning, Federated Learning, Edge Intelligence, Model Adaptation, Real-time Processing, Autonomous Systems, Resource Allocation

Synopsis

The increasing complexity and scale of distributed computing environments necessitate intelligent and adaptive systems for optimal decision making. This study presents an integrated approach to designing machine learning (ML) architectures that adapt dynamically to changes in distributed systems to ensure robust, real-time, and scalable decision making. Emphasis is placed on adaptive reinforcement learning (RL), federated learning (FL), and modular deep learning models that respond to system-level variances such as node failure, latency shifts, or computational bottlenecks. We demonstrate a conceptual framework and present key literature advancements supporting such architectures. The paper concludes by outlining future trajectories and open challenges for scalable intelligent systems in heterogeneous environments.

References

[1] Zhang, Y., Chen, X., & Lin, Q. (2013). Adaptive gradient methods for distributed optimization. IEEE Transactions on Neural Networks, 24(12), 2875–2887.

[2] Dean, J., Corrado, G., & Monga, R. (2012). Large scale distributed deep networks. Advances in Neural Information Processing Systems, 25(1), 1223–1231.

[3] Gundaboina A. Data Loss Prevention in Healthcare: Advanced Strategies for Protecting PHI in Cloud Environments. Journal of Artificial Intelligence, Machine Learning and Data Science 2023 1(2), 3045-3051. DOI: doi.org/10.51219/JAIMLD/anjan-gundaboina/628

[4] Bonawitz, K., Eichner, H., & Ramage, D. (2017). Practical secure aggregation for federated learning. Proceedings of the 34th International Conference on Machine Learning, 70(1), 1–9.

[5] Gundaboina, A. (2024). HITRUST Certification Best Practices: Streamlining Compliance for Healthcare Cloud Solutions. International Journal of Computer Science and Information Technology Research, 5(1), 76–94. https://ijcsitr.org/index.php/home/article/view/IJCSITR_2024_05_01_008

[6] Li, K., Zhao, M., & Ma, X. (2019). Adaptive hierarchical reinforcement learning for task scheduling in fog computing. IEEE Internet of Things Journal, 6(4), 6355–6365.

[7] Yu, H., Yang, X., & Wang, S. (2020). Adaptive federated learning in resource-constrained edge environments. ACM Transactions on Intelligent Systems, 11(2), 45–67.

[8] Uppuluri, V. (2023). Design and Deployment of Predictive Models for Influenza Breakthrough Infections Using Pharmacy Test Data. Journal of Artificial Intelligence, Machine Learning & Data Science, 1(2), 3031–3037. https://doi.org/10.51219/JAIMLD/vijitha-uppuluri/626

[9] Wang, L., & Joshi, R. (2021). Multi-level orchestration of adaptive ML in edge-cloud environments. IEEE Transactions on Cloud Computing, 9(3), 254–263.

[10] Luo, Y., Cheng, J., & Tan, J. (2018). On the convergence of model averaging in federated learning. IEEE Journal on Selected Areas in Communications, 36(6), 1220–1233.

[11] Potla, R.B. (2023). Supplier Collaboration Portals for Component Manufacturers: Procure-to-Pay Automation and Working-Capital Outcomes. International Journal of Artificial Intelligence (ISCSITR-IJAI), 4(1), 16–40. https://doi.org/10.63397/ISCSITR-IJAI_04_01_002

[12] He, X., Chen, W., & Li, T. (2016). Decentralized deep learning via adaptive gradient exchange. Pattern Recognition Letters, 78(1), 31–37.

[13] Ramezani, M., Ghobaei-Arani, M., & Souri, A. (2020). Resource provisioning using adaptive ML in edge computing. Journal of Network and Computer Applications, 156(3), 102–115.

[14] Vallemoni, R.K. (2023). Merchant Onboarding and Risk Scoring: Data Governance, Master Data, and Golden-Record Strategies. ISCSITR - International Journal of Scientific Research in Information Technology (ISCSITR-IJSRIT), 4(1), 16–41. https://doi.org/10.63397/ISCSITR-IJSRIT_04_01_002

[15] Sahu, A., Aggarwal, A., & Ghosh, M. (2021). Secure and adaptive federated learning for healthcare. Computer Methods and Programs in Biomedicine, 205(1), 106–115.

[16] Kumar, R., Singh, V., & Rao, N. (2017). Adaptive distributed learning: Algorithms and performance. Neurocomputing, 226(5), 62–73.

[17] Thompson, P., Miller, J., & Raj, S. (2019). Multi-agent reinforcement learning in distributed systems. ACM Journal on Emerging Technologies in Computing Systems, 15(4), 25–37.

[18] Vallemoni, R.K. (2023). Data Lineage and Metadata in Payment Ecosystems: Auditability and Regulatory Readiness across the Life Cycle. Frontiers in Computer Science and Artificial Intelligence, 2(1), 46–58. https://doi.org/10.32996/fcsai.2023.2.1.5

[19] Chen, M., Hao, Y., & Zhang, Y. (2020). Edge intelligence: Pushing AI to the edge. IEEE Access, 8(1), 1927–1941.

[20] Zhao, J., Wang, Y., & Hu, K. (2022). Adaptive streaming of ML models in cloud systems. Journal of Cloud Computing, 11(1), 31–45.

[21] Ahmad, F., Nazir, M., & Qureshi, H. (2018). Scalable architectures for intelligent edge computing. Future Generation Computer Systems, 87(2), 704–712.