3 papers
quant-ph2025
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia +2
The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often re…
cs.LG2025
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources
Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier +2
Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protect…
quant-ph2024
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
Siddhant Dutta, Pavana P Karanth, Pedro Maciel Xavier +5
The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federate…