WP3. AI-based distributed algorithms for multimodal perception and situational awareness


25 months, June 1st 2021 to June 30th 2023


MARVEL will deliver an AI-based framework that will provide multimodal perception of audio-visual scenes and will employ advanced Machine Learning model distribution and decision offloading mechanisms. These techniques will enable fast reaction to data streams and will help decision makers gain situational awareness in short times. The WP3 objectives are to

  • define and execute measures ensuring privacy preservation during data processing.
  • follow a personalized federated learning approach to train and execute ML models at the Edge, Fog and Cloud.
  • train new or updated ML algorithms for audio-visual classification and analytics.
  • optimise ML deployment in the E2F2C infrastructure in a continuous manner.
  • deploy ML algorithms at all layers of E2F2C.
  • implement light-weight ML models for deployment at the edge.


NoTitleLeaderTypeDownload linkDue Date
D3.1Multimodal and privacy-aware audio-visual intelligence – initial versionAUDemoTo be releasedM06, June 30th, 2021
D3.2Efficient deployment of AI-optimised ML/DL models – initial versionFORTHDemoTo be releasedM18, June 30th, 2022
D3.3E2F2C Privacy preservation mechanismsFBKDemoTo be releasedM18, June 30th, 2022
D3.4MARVEL’s federated learning realizationUNSDemoTo be releasedM30, June 30th, 2023
D3.5Multimodal and privacy-aware audio-visual intelligence – final versionAUDemoTo be releasedM36, Dec 31st, 2023
D3.6Efficient deployment of AI-optimised ML/DL models – final versionFORTHDemoTo be releasedM36, Dec 31st, 2023

Key Facts

  • Project Coordinator: Dr. Sotiris Ioannidis
  • Institution: Foundation for Research and Technology Hellas (FORTH)
  • E-mail: marvel-info@marvel-project.eu 
  • Start: 01.01.2021
  • Duration: 36 months
  • Participating Organisations: 17
  • Number of countries: 12

Get Connected



This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.