Displaying 19551 - 19600 of 37174
| Request date Sort ascending | Organisation name | Country | Search type | Topic | Link | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 06/04/2023 | Exeo Lab Srl | 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 | 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 | |||||||||||
| 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] | |||||||||||
| 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 | Improving the global demand supply forecast of the semiconductor supply chain (IA) (HORIZON-KDT-JU-2023-3-IA-TOPIC-1) | 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 | 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. |
|||||||||||