Copertina

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Digregorio, Gabriele, Bleggi, Francesco, Caroli, Federico, Carminati, Michele, Zanero, Stefano, Longari, Stefano
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
di: Digregorio, Gabriele, et al.
Pubblicazione: (2025)

On the (In)Security of Loading Machine Learning Models
di: Digregorio, Gabriele, et al.
Pubblicazione: (2025)

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
di: Maffiola, Davide, et al.
Pubblicazione: (2025)

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
di: Longari, Stefano, et al.
Pubblicazione: (2025)

CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment
di: Longari, Stefano, et al.
Pubblicazione: (2025)

TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
di: Di Gennaro, Marco, et al.
Pubblicazione: (2025)

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
di: Digregorio, Gabriele, et al.
Pubblicazione: (2025)

Model Predictive Control with adaptive resilience for Denial-of-Service Attacks mitigation on a Regulated Dam
di: Cestari, Raffaele Giuseppe, et al.
Pubblicazione: (2024)

An Anomaly Detection System Based on Generative Classifiers for Controller Area Network
di: Zhao, Chunheng, et al.
Pubblicazione: (2024)

How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
di: Panebianco, Francesco, et al.
Pubblicazione: (2025)

The Illusion of Randomness: An Empirical Analysis of Address Space Layout Randomization Implementations
di: Binosi, Lorenzo, et al.
Pubblicazione: (2024)

Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection
di: Di Gennaro, Marco, et al.
Pubblicazione: (2025)

PackHero: A Scalable Graph-based Approach for Efficient Packer Identification
di: Di Gennaro, Marco, et al.
Pubblicazione: (2025)

Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
di: Bellante, Armando, et al.
Pubblicazione: (2025)

Poster: libdebug, Build Your Own Debugger for a Better (Hello) World
di: Digregorio, Gabriele, et al.
Pubblicazione: (2025)

LeakSealer: A Semisupervised Defense for LLMs Against Prompt Injection and Leakage Attacks
di: Panebianco, Francesco, et al.
Pubblicazione: (2025)

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
di: Kavuri, Likhitha Annapurna, et al.
Pubblicazione: (2025)

Poster: Towards an Automated Security Testing Framework for Industrial UEs
di: Michaelides, Sotiris, et al.
Pubblicazione: (2025)

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
di: Han, Shanshan, et al.
Pubblicazione: (2023)

FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios
di: Narkedimilli, Sathwik, et al.
Pubblicazione: (2024)

Simulation in Cybersecurity: Understanding Techniques, Applications, and Goals
di: Serena, Luca, et al.
Pubblicazione: (2025)

FL-DECO-BC: A Privacy-Preserving, Provably Secure, and Provenance-Preserving Federated Learning Framework with Decentralized Oracles on Blockchain for VANETs
di: Narkedimilli, Sathwik, et al.
Pubblicazione: (2024)

FedMicro-IDA: A Federated Learning and Microservices-based Framework for IoT Data Analytics
di: Atitallah, Safa Ben, et al.
Pubblicazione: (2025)

FedSelect-ME: A Secure Multi-Edge Federated Learning Framework with Adaptive Client Scoring
di: Vatani, Hanie, et al.
Pubblicazione: (2025)

FedMUP: Federated Learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments
di: Gupta, Kishu, et al.
Pubblicazione: (2024)

FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
di: S, Devika, et al.
Pubblicazione: (2025)

Decentralized Federated Learning: A Survey on Security and Privacy
di: Hallaji, Ehsan, et al.
Pubblicazione: (2024)

FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks
di: Nguyen, Duong H., et al.
Pubblicazione: (2024)

A Formal Model of Security Controls' Capabilities and Its Applications to Policy Refinement and Incident Management
di: Basile, Cataldo, et al.
Pubblicazione: (2024)

FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
di: Yang, Xu, et al.
Pubblicazione: (2024)

FedFDP: Fairness-Aware Federated Learning with Differential Privacy
di: Ling, Xinpeng, et al.
Pubblicazione: (2024)

FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
di: Yang, Yuxin, et al.
Pubblicazione: (2024)

CrossLink: A Decentralized Framework for Secure Cross-Chain Smart Contract Execution
di: Hossain, Tahrim, et al.
Pubblicazione: (2025)

Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators
di: Bottoni, Simone, et al.
Pubblicazione: (2025)

A Survey on Decentralized Federated Learning
di: Gabrielli, Edoardo, et al.
Pubblicazione: (2023)

A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
di: Feng, Chao, et al.
Pubblicazione: (2025)

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
di: Chen, Jian, et al.
Pubblicazione: (2025)

FedNC: A Secure and Efficient Federated Learning Method with Network Coding
di: Shi, Yuchen, et al.
Pubblicazione: (2023)

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
di: Xhemrishi, Marvin, et al.
Pubblicazione: (2023)

Voltran: Unlocking Trust and Confidentiality in Decentralized Federated Learning Aggregation
di: Wang, Hao, et al.
Pubblicazione: (2024)