An Open-Source AI-as-a-Service Framework for Federated, Efficient, and Drift-Robust Learning in the Continuum Edge-Cloud

Abstract

The rise of edge computing has enabled diverse IoT applications while introducing new challenges in latency, resource constraints, and continuous model adaptations across the cloud, edge, and IoT continuum. Addressing these issues requires optimizing the trade-off between efficiency and latency on resource-constrained hardware, fostering collaboration, and advancing model compression and green computing to reduce computational overhead. We introduce OASIS, an open-source, library-agnostic framework for scalable Edge Machine Learning that unifies predictive analytics, model compression, and supports FL for privacy-preserving training in distributed environments. Designed for real-time forecasting and adaptive monitoring, OASIS enables lightweight ML deployments in dynamic settings with limited resources. OASIS simplifies adoption by offering modular APIs and pre-integrated tools, allowing users to plug in models or connect telemetry data with minimal configuration, making it suitable for practitioners across domains. Our implementation integrates drift detection, SHAP-based explainability, and end-to-end MLOps and monitoring via MLFlow and NannyML. We have presented illustrative examples using real and synthetic data, particularly synthetic CPU telemetry, to stress-test the robustness of the system and demonstrate improvements in inference speed, memory efficiency, and fault resilience. By consolidating critical AI capabilities into a single interface, OASIS lowers the barrier for deploying robust, adaptive, and FL applications at the edge. The code is publicly available here: https://github.com/icos-project/intelligence-module.