Edge-Cloud Hybrid Data Pipelines: Architectures for Federated Analytics and Learning
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I3P103Keywords:
Edge Computing, Cloud Computing, Federated Learning, Data Pipelines, Hybrid Architecture, Real-Time Analytics, Edge AI, Privacy-Preserving Computation, Distributed Data Processing, Iot, Model Aggregation, Orchestration FrameworksAbstract
The paper discusses the importance of seamless integration between edge and cloud systems in edge-cloud hybrid data pipelines for the growing role of federated analytics and machine learning. The conjunction of edge computing (hardware near the data source) with the cloud (that has scalable resources) has allowed the definition of new data analysis methods at scale, in real time and with a higher respect for privacy. Those hybrid approaches in federated learning and analytics scenarios can allow the information to remain scattered, which means that the safety and the respecting of the rules can be improved. Also, not only do they still allow the global insight or the coordinating but in addition, the model training and the analytics orchestration are going on. The paper shows that the architectures have to support continuous safe & low-latency data flows through a wide variety of edge nodes and cloud platforms during these network conditions that change. Real-time decision-making, anomaly detection, and adaptive model retraining are just a few examples of applications that benefit from this synergy. Several architectural patterns are dissected in this paper to illustrate the design trade-offs in such parts as workload distribution, data synchronization, orchestration, and trust boundaries. We also touch on the employment of containerization, message queues, federated model aggregation, and privacy-preserving mechanisms to ensure that these data pipelines remain scalable, resilient, and secure. In the end, this work lays down a detailed framework for designing edge-cloud hybrid systems for carrying out intelligent, federated operations, which units are thus able to effectively manage distributed data while still providing the same levels of performance, security, and regulatory compliance.
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