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Created page with "==EU Project short name::AI-SPRINT== ===EU Project full name::Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum=== '''Full project details (EU Research results portal):''' CORDIS URL::https://cordis.europa.eu/project/id/101016577 === '''Project description:''' === Artificial Intelligence (AI), to become fully pervasive, needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge..."
 
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[[Programme::Horizon 2020]]
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Latest revision as of 12:56, 8 May 2026

AI-SPRINT

Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum

Full project details (EU Research results portal): https://cordis.europa.eu/project/id/101016577

Project description:

Artificial Intelligence (AI), to become fully pervasive, needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge computing is close to where data are produced and therefore crucial to their timely, flexible, and secure management. AI-SPRINT will define a framework for developing AI applications in computing continua, enabling a finely-tuned tradeoff between performance (e.g. in terms of end-to-end latency and throughput) and AI model accuracy, while providing security and privacy guarantees. AI-SPRINT outcomes are: i) simplified programming models to reduce the steep learning curves in the development of AI software in computing continua; ii) highly specialized building blocks for distributed training, privacy preservation and advanced machine learning models, to shorten time-to-market for AI applications; iii) automated deployment and dynamic reconfiguration to decrease the cost of operating AI software. Beneficiaries include end-users of AI systems, software developers, system integrators, and cloud providers. AI-SPRINT tools will make it possible to consider security and privacy early in the design stage and to seamlessly manage the time-varying conditions typical of real environments. Real-world scenarios are an integral part of AI-SPRINT as key to guiding requirements and development and validating results. Three heterogeneous use cases (farming 4.0, maintenance & inspection, and personalized healthcare) are built by industrial partners. Cutting-edge innovation is brought to the Consortium by four research partners with complementary expertise. Two system integrators provide vision on relevant verticals and technology insights, one cloud provider brings real-world implementation expertise, and two specialists in dissemination ensure impacts and uptake. AI-SPRINT will also pursue a sustainability path through the creation of an Alliance and Adopter Acceleration club as a marketplace for AI businesses

EuroVoc IDs: /natural sciences/computer and information sciences/software

EU Programme: Horizon 2020

EU Project

Project publications:

EU ProjectHas TitleHas CategoryHas TypeHas YearHas DOI
AI-SPRINTSecure execution of ML workflows on the Computing ContinuumArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3578245.3584727
AI-SPRINTSinClave: Hardware-assisted Singletons for TEEsArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3590140.3629107
AI-SPRINTA Sorted Datalog Hammer for Supervisor Verification Conditions Modulo Simple Linear ArithmeticArtificial Intelligence and Machine Learning SystemsConference proceedings2022https://doi.org/10.1007/978-3-030-99524-9 27
AI-SPRINTOSCAR-P and aMLLibrary: Performance Profiling and Prediction of Computing Continua ApplicationsArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3578245.3584941
AI-SPRINTSecuring the Execution of ML Workflows across the Compute ContinuaArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3578245
AI-SPRINTA Last-Level Defense for Application Integrity and ConfidentialityArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.48550/arxiv.2311.06154
AI-SPRINTA Datalog Hammer for Supervisor Verification Conditions Modulo Simple Linear ArithmeticArtificial Intelligence and Machine Learning SystemsConference proceedings2021https://doi.org/10.1007/978-3-030-86205-3
AI-SPRINTPerun: Confidential Multi-stakeholder Machine Learning Framework with Hardware Acceleration SupportArtificial Intelligence and Machine Learning SystemsConference proceedings2021https://doi.org/10.1007/978-3-030-81242-3 11
AI-SPRINTTaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a ServiceArtificial Intelligence and Machine Learning SystemsPeer reviewed articles2021https://doi.org/10.1109/access.2021.3109972
AI-SPRINTOn the Acceleration of FaaS Using Remote GPU VirtualizationArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3578245.3584933
AI-SPRINTCapacity Planning for Dependable ServicesArtificial Intelligence and Machine Learning SystemsPeer reviewed articles2023https://doi.org/10.1016/j.tcs.2023.114126
AI-SPRINTDiscriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural NetworksArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.48550/arxiv.2306.03054
AI-SPRINTEnhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early ExitsArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.48550/arxiv.2302.01888
AI-SPRINTTrustworthy confidential virtual machines for the massesArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3590140.3629124
AI-SPRINTHeterogeneous Datasets for Federated Survival Analysis SimulationArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3578245.3584935
AI-SPRINTChallenges Towards Modeling and Generating Infrastructure-as-CodeArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1145/3578245.3584937
AI-SPRINTSGDE: Secure Generative Data Exchange for Cross-Silo Federated LearningArtificial Intelligence and Machine Learning SystemsConference proceedings2022https://doi.org/10.1145/3573942.3573974
AI-SPRINTFormal Foundations for Intel SGX Data Center Attestation PrimitivesArtificial Intelligence and Machine Learning SystemsConference proceedings2021https://doi.org/10.13140/rg.2.2.36760.21768
AI-SPRINTADAM-CS - Advanced Asynchronous Monotonic Counter ServiceArtificial Intelligence and Machine Learning SystemsConference proceedings2021https://doi.org/10.1109/dsn48987.2021.00053
AI-SPRINTAnticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early ExitsArtificial Intelligence and Machine Learning SystemsConference proceedings2023https://doi.org/10.1016/j.procs.2023.08.190
AI-SPRINTServerless Workflows for Containerised Applications in the Cloud ContinuumArtificial Intelligence and Machine Learning SystemsPeer reviewed articles2021https://doi.org/10.1007/s10723-021-09570-2
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