A Federated Learning Approach to Distributed DevOps Automation in Platform Engineering Architectures
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P120Keywords:
Federated Learning, DevOps Automation, Platform Engineering, AIOps, Distributed Systems, Kubernetes, CI/CD, Edge Computing, Cloud-Native Architecture, ObservabilityAbstract
DevOps has shifted from a centralized and automation-driven system to a highly distributed, intelligent, and autonomous system of systems that are driven by cloud-native systems. Traditional DevOps automation pipelines, however, are hindered by centralized telemetry processing, a lack of cross-organization learning and privacy issues in multi-tenant environments. This paper introduces a Federated Learning (FL)-based DevOps automation framework for distributed platform engineering architectures to overcome these limitations. The proposed approach allows multiple DevOps environments (microservices clusters, CI/CD pipelines, Kubernetes-based platforms, etc.) to train machine learning models together, and avoid sharing raw operational data. Rather, only model updates (gradients or weights) are shared with a central aggregator, keeping data private but allowing global intelligence. This paradigm is useful for anomaly detection, predicting scaling, incident classification, and deployment optimization in distributed systems. The architecture brings together FL and DevOps toolchains like GitOps pipelines, observability stacks (Prometheus, Grafana), and infrastructure-as-code systems (Terraform, Helm). A hierarchical federated orchestration layer is added for model aggregation, drift correction, and client selection. In addition, adaptive optimization methods like FedAvg and FedProx are used to enhance convergence for non-IID (Non-Independent and Identically Distributed) system telemetry data. Experimental testing shows that the FL-based DevOps automation framework significantly mitigates mean incident response time, increases the precision of anomaly detection and reduces the downtime of the system when compared to the traditional centralized AIOps method. The results are also notable for the increased scalability in multi-cloud configurations, and for better privacy protection within GDPR-like environments. The research finds that federated learning can be integrated into DevOps and platform engineering architectures to create a scalable, privacy-preserving, and intelligent automation layer that enhances operational resilience and system reliability in today's distributed computing landscape.
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