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Found 9 papers, 1 papers with code
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning
In particular, the proposed attack can be carried out by one of the clients during the Federated Learning phase of FTL by identifying the optimal local for the trigger through XAI and encapsulating compressed information of the backdoor class.
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data.
A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
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, increasing systems' complexity, and the need for reliability and autonomy.
The SemIoE Ontology: A Semantic Model Solution for an IoE-based Industry
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
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models.
A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegation
With the number of connected smart devices expected to constantly grow in the next years, Internet of Things (IoT) solutions are experimenting a booming demand to make data collection and processing easier.
Label Inference Attacks against Node-level Vertical Federated GNNs
In this work, we are the first (to the best of our knowledge) to investigate label inference attacks on VFL using a zero-background knowledge strategy.
Predicting Tweet Engagement with Graph Neural Networks
Social Networks represent one of the most important online sources to share content across a world-scale audience.
Turning Privacy-preserving Mechanisms against Federated Learning
Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities.
