77 citations · 171 across the 24 of their papers we have counts for
6 papers · 1 filter
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning
Jiaxin Li, Marco Arazzi, Antonino Nocera +1
Source Inference Attack (SIA) in Federated Learning (FL) aims to identify which client used a target data point for local model training. It allows the central server to audit clie…
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning
Marco Arazzi, Stefanos Koffas, Antonino Nocera +1
Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out b…
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VF…
A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increa…
The SemIoE Ontology: A Semantic Model Solution for an IoE-based Industry
Marco Arazzi, Antonino Nocera, Emanuele Storti
Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware and user-centric digital systems…
Privacy-Preserving in Blockchain-based Federated Learning Systems
Sameera K. M., Serena Nicolazzo, Marco Arazzi +4
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants…