3 citations · 3 across the 8 of their papers we have counts for
9 papers
GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning
Daniel M. Jimenez-Gutierrez, Albenzio Cirillo, Raffaele Nicolussi +2
We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIG…
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh +3
Federated Learning (FL) enables distributed Artificial Intelligence (AI) across cloud-edge environments by allowing collaborative model training without centralizing data. In cross…
A Proof of Concept for a Digital Twin of an Ultrasonic Fermentation System
Francesco Saverio Sconocchia Pisoni, Andrea Vitaletti, Davide Appolloni +4
This paper presents the design and implementation of a proof of concept digital twin for an innovative ultrasonic-enhanced beer-fermentation system, developed to enable intelligent…
Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learning
Daniel M. Jimenez-Gutierrez, Mehrdad Hassanzadeh, David Solans +5
Federated learning (FL) supports privacy-preserving, decentralized machine learning (ML) model training by keeping data on client devices. However, non-independent and identically…
On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Daniel M. Jimenez-Gutierrez, Yelizaveta Falkouskaya, Jose L. Hernandez-Ramos +3
Federated Learning (FL) is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While…
Efficient Query Verification for Blockchain Superlight Clients Using SNARKs
Stefano De Angelis, Ivan Visconti, Andrea Vitaletti +1
Blockchains are among the most powerful technologies to realize decentralized information systems. In order to safely enjoy all guarantees provided by a blockchain, one should main…
A Thorough Assessment of the Non-IID Data Impact in Federated Learning
Daniel M. Jimenez-Gutierrez, Mehrdad Hassanzadeh, Aris Anagnostopoulos +2
Federated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients' information, ensuring data privacy. The decentralized nature of FL de…
Decentralized Fair Exchange with Advertising
Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti +1
Before a fair exchange takes place, there is typically an advertisement phase with the goal of increasing the appeal of possessing a digital asset while keeping it sufficiently hid…
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions
Daniel M. Jimenez G., David Solans, Mikko Heikkila +4
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively tra…