Displaying 23851 - 23900 of 41583
| Request date Sort ascending | Organisation name | Country | Search type | Topic | Link | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 04/04/2023 | NUROMEDIA GMBH | Germany | Expertise Request | System Architecture (HORIZON-JU-SNS-2023-STREAM-B-01-01) | 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] | |||||||||||
| 04/04/2023 | NUROMEDIA GMBH | Germany | Expertise Request | Communication Infrastructure Technologies and Devices (HORIZON-JU-SNS-2023-STREAM-B-01-03) | 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] | |||||||||||
| 04/04/2023 | NUROMEDIA GMBH | Germany | Expertise Request | Complementary SNS experimental Pan-EU federated Infrastructure (HORIZON-JU-SNS-2023-STREAM-C-01-01) | 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] | |||||||||||
| 04/04/2023 | Consortium Ubiquitous Technologies S.c.a.r.l. | Italy | Expertise Request | System Architecture (HORIZON-JU-SNS-2023-STREAM-B-01-01) | F&T portal | ||||||
| Cubit is a SME specialised in the design and realisation of custom electronic devices, monitoring and control systems, and IoT solutions for industrial automation, smart city, wearables, etc. We cover from hardware design to software development, including complex and intelligent algorithms on resource-constrained devices, interoperability and integration with larger systems. We have experience with numerous wired / wireless communication technologies, including design of proprietary protocols. | |||||||||||
| 04/04/2023 | UNIVERSITATSMEDIZIN ROSTOCK | Germany | Expertise Request | Pilot line(s) for 2D materials-based devices (RIA) (HORIZON-CL4-2024-DIGITAL-EMERGING-01-31) | F&T portal | ||||||
| The Institute for Biomedical Engineering (IBMT) at Rostock University Medical Center performs research in the area of biomaterials and implant technology. We design and prototype novel implantable medical devices, implant-based coatings for local drug delivery, as well as functionalized biomaterials. We run a GLP-certified lab for preclinical (in vitro, in vivo) biocompatibility testing and combination product analytics (drug release, stability, degradation).More info: ibmt.med.uni-rostock.de/en | |||||||||||