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Request date Organisation name Country Search type Topic Link
07/04/2023 BEIA CONSULT INTERNATIONAL SRL Romania Expertise Request Coordination of the European software-defined vehicle platform (CSA) (HORIZON-KDT-JU-2023-3-CSA-TOPIC-3) F&T portal
R&D performing SME founded in 1991, leading ICT integrator in Romania with offices in Austria and Belgium. BEIA is ISO certified with experience in coordinating & participating in over 60 R&D Innovation projects (FP6, FP7, Horizon, Eureka, ERA-Net, etc.) on cloud, IoT, AI, 5G, blockchain/quantum/security (3DSafeGuard, 5G-SAFE+, ALADIN, Arrowhead, HUBCAP, S4ALLCITIES, SealedGRID, SWITCH, SOLID-B5G, SARWS, FOR-FREIGHT, FLEXI-CROSS, EV-BAT, UPSIM, VITAL-5G, etc.): [email protected] / www.beia.eu
06/04/2023 MINDS & SPARKS GMBH Austria Expertise Request Improving the global demand supply forecast of the semiconductor supply chain (IA) (HORIZON-KDT-JU-2023-3-IA-TOPIC-1) F&T portal
M&S is an experienced project partner with a skilled team of researchers, engineers, developers and creatives offering APPLIED RESEARCH: Stakeholder Analysis, User Surveys, Requirements Engineering. DEVELOPMENT: Conception, Web Applications, Interface Design, Data Visualization, Piloting, Validation. DISSEMINATION & EXPLOITATION: Websites, Communication, Innovation and Technology Marketing, Solution Assessments, Market Analysis, Exploitation Planning. OUR PROFILE: http://bit.ly/mindsandsparks
06/04/2023 MINDS & SPARKS GMBH Austria Expertise Request Wireless Communication Technologies and Signal Processing (HORIZON-JU-SNS-2023-STREAM-B-01-02) F&T portal
M&S is an experienced project partner with a skilled team of researchers, engineers, developers and creatives offering APPLIED RESEARCH: Stakeholder Analysis, User Surveys, Requirements Engineering. DEVELOPMENT: Conception, Web Applications, Interface Design, Data Visualization, Piloting, Validation. DISSEMINATION & EXPLOITATION: Websites, Communication, Innovation and Technology Marketing, Solution Assessments, Market Analysis, Exploitation Planning. OUR PROFILE: http://bit.ly/mindsandsparks
06/04/2023 EXEO LAB S.R.L. Italy Expertise Request Facilitate the engagement in global ICT standardisation development (CSA) (HORIZON-CL4-2024-HUMAN-01-61) F&T portal
Exeo Lab is a partnership of policy specialists, with large experience in the areas of information technologies. We also provide SMEs and start-ups with advanced support in the field of digital and industrial technologies. We are interested in collaborating in the proposal writing and make available our national and EU network of partners and stakeholders. Please, contact us at [email protected]
06/04/2023 MINDS & SPARKS GMBH Austria Expertise Request Focus Topic on Hardware abstraction layer for a European Vehicle Operating System (RIA) (HORIZON-KDT-JU-2023-2-RIA-FOCUS-TOPIC-2) F&T portal
M&S is an experienced project partner with a skilled team of researchers, engineers, developers and creatives offering APPLIED RESEARCH: Stakeholder Analysis, User Surveys, Requirements Engineering. DEVELOPMENT: Conception, Web Applications, Interface Design, Data Visualization, Piloting, Validation. DISSEMINATION & EXPLOITATION: Websites, Communication, Innovation and Technology Marketing, Solution Assessments, Market Analysis, Exploitation Planning. OUR PROFILE: http://bit.ly/mindsandsparks
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 NUROMEDIA GMBH Germany Expertise Request Coordination of the European software-defined vehicle platform (CSA) (HORIZON-KDT-JU-2023-3-CSA-TOPIC-3) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 TISALABS LIMITED Ireland Expertise Request Reliable Services and Smart Security (HORIZON-JU-SNS-2023-STREAM-B-01-04) F&T portal
We are looking to participate into this call and we would like to get with organizations that have a use case.
We are a security company focusing on edge software deployment and management as well as running AI/ML at the dge.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Ayuntamiento de Benetússer Spain Expertise Request SNS Societal Challenges (HORIZON-JU-SNS-2023-STREAM-CSA-01) F&T portal
Benetússer Town Council, which belongs to the metropolitan area of Valencia, has its own department of European projects. We work transversally with the different areas in the Town Council, such as equality, youth, participation, education, environment, urbanism, employment and development, among others, as well as with the civic organizations of the metropolitan area. We can add value and help increase the quality of European projects. If you are interested, contact [email protected]