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The explosion of data volumes generated by an increasing number ofapplications is strongly impacting the evolution of distributed digitalinfrastructures for data analytics and machine learning (ML). While dataanalytics used to be mainly performed on cloud infrastructures, the rapiddevelopment of IoT infrastructures and the requirements for low-latency, secureprocessing has motivated the development of edge analytics. Today, to balancevarious trade-offs, ML-based analytics tends to increasingly leverage aninterconnected ecosystem that allows complex applications to be executed onhybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPCsystems in what is called the Computing Continuum, the Digital Continuum, orthe Transcontinuum.Enabling learning-based analytics on such complexinfrastructures is challenging. The large scale and optimized deployment oflearning-based workflows across the Edge-to-Cloud Continuum requires extensiveand reproducible experimental analysis of the application execution onrepresentative testbeds. This is necessary to help understand the performancetrade-offs that result from combining a variety of learning paradigms andsupportive frameworks. A thorough experimental analysis requires the assessmentof the impact of multiple factors, such as: model accuracy, training time,network overhead, energy consumption, processing latency, among others.Thisreview aims at providing a comprehensive vision of the main state-of-the-artlibraries and frameworks for machine learning and data analytics availabletoday. It describes the main learning paradigms enabling learning-basedanalytics on the Edge-to-Cloud Continuum. The main simulation, emulation,deployment systems, and testbeds for experimental research on the Edge-to-CloudContinuum available today are also surveyed. Furthermore, we analyze how theselected systems provide support for experiment reproducibility. We concludeour review with a detailed discussion of relevant open research challenges andof future directions in this domain such as: holistic understanding ofperformance; performance optimization of applications;efficient deployment ofArtificial Intelligence (AI) workflows on highly heterogeneous infrastructures;and reproducible analysis of experiments on the Computing Continuum.

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review