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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.
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	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	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.
04/04/2023	NUROMEDIA GMBH	Vatican City	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.