3 citations · 3 across the 3 of their papers we have counts for
8 papers
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…