# Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review arXiv: https://arxiv.org/abs/2205.01081 ยท HTML: https://mlsanity.com/paper/2205.01081/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 181 references that are in the map. - First posted on arXiv 29 Apr 2022. ## Builds on (most cited first, 28 of 28) - [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017) - [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017) - [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019) - [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018) - [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018) - [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019) - [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018) - [Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence](https://mlsanity.com/paper/1909.00560/) (2019) - [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017) - [A First Look at Deep Learning Apps on Smartphones](https://mlsanity.com/paper/1812.05448/) (2018) - [Spiking Neural Networks and Online Learning: An Overview and Perspectives](https://mlsanity.com/paper/1908.08019/) (2019) - [Hyperparameter Search in Machine Learning](https://mlsanity.com/paper/1502.02127/) (2015) - [A Survey on Edge Computing Systems and Tools](https://mlsanity.com/paper/1911.02794/) (2019) - [All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey](https://mlsanity.com/paper/1808.05283/) (2018) - [pCAMP: Performance Comparison of Machine Learning Packages on the Edges](https://mlsanity.com/paper/1906.01878/) (2019) - [iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments](https://mlsanity.com/paper/1606.02007/) (2016) - [Deep Learning in the Era of Edge Computing: Challenges and Opportunities](https://mlsanity.com/paper/2010.08861/) (2020) - [Training on the Edge: The why and the how](https://mlsanity.com/paper/1903.03051/) (2019) - [YAFS: A simulator for IoT scenarios in fog computing](https://mlsanity.com/paper/1902.01091/) (2019) - [Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges](https://mlsanity.com/paper/1911.01941/) (2019) - [The Disruptions of 5G on Data-driven Technologies and Applications](https://mlsanity.com/paper/1909.08096/) (2019) - [Exploring Task Placement for Edge-to-Cloud Applications using Emulation](https://mlsanity.com/paper/2104.03368/) (2021) - [EmuFog: Extensible and Scalable Emulation of Large-Scale Fog Computing Infrastructures](https://mlsanity.com/paper/1709.07563/) (2017) - [Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo](https://mlsanity.com/paper/2010.03035/) (2020) - [IoTSim-Edge: A Simulation Framework for Modeling the Behaviour of IoT and Edge Computing Environments](https://mlsanity.com/paper/1910.03026/) (2019) - [IoT Stream Processing and Analytics in The Fog](https://mlsanity.com/paper/1705.05988/) (2017) - [S2CE: A Hybrid Cloud and Edge Orchestrator for Mining Exascale Distributed Streams](https://mlsanity.com/paper/2007.01260/) (2020) - [Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum](https://mlsanity.com/paper/2108.04033/) (2021) ## Built on by (most cited first, 6 of 6) - [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025) - [Follow-Me AI: Energy-Efficient User Interaction with Smart Environments](https://mlsanity.com/paper/2404.12486/) (2024) - [Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars](https://mlsanity.com/paper/2406.17812/) (2024) - [Screen Before You Fetch: Compressed Byzantine Screening for Decentralized Learning on the Edge-Cloud Continuum](https://mlsanity.com/paper/2510.07922/) (2025) - [Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization](https://mlsanity.com/paper/2406.09478/) (2024) - [Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges](https://mlsanity.com/paper/2507.17903/) (2025) ## Abstract (arXiv) The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data analytics used to be mainly performed on cloud infrastructures, the rapid development of IoT infrastructures and the requirements for low-latency, secure processing has motivated the development of edge analytics. Today, to balance various trade-offs, ML-based analytics tends to increasingly leverage an interconnected ecosystem that allows complex applications to be executed on hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC systems in what is called the Computing Continuum, the Digital Continuum, or the Transcontinuum.Enabling learning-based analytics on such complex infrastructures is challenging. The large scale and optimized deployment of learning-based workflows across the Edge-to-Cloud Continuum requires extensive and reproducible experimental analysis of the application execution on representative testbeds. This is necessary to help understand the performance trade-offs that result from combining a variety of learning paradigms and supportive frameworks. A thorough experimental analysis requires the assessment of the impact of multiple factors, such as: model accuracy, training time, network overhead, energy consumption, processing latency, among others.This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. It describes the main learning paradigms enabling learning-based analytics on the Edge-to-Cloud Continuum. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed. Furthermore, we analyze how the selected systems provide support for experiment reproducibility. We conclude our review with a detailed discussion of relevant open research challenges and of future directions in this domain such as: holistic understanding of performance; performance optimization of applications;efficient deployment of Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures; and reproducible analysis of experiments on the Computing Continuum.