Quantifying uncertainty in adversarial federated learning

• Grant number: 23/00721-1
• Support Opportunities: Regular Research Grants
• Start date: August 01, 2023
• End date: July 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Heitor Soares Ramos Filho
• Grantee: Heitor Soares Ramos Filho
• Host Institution: Instituto de Ciências Exatas (ICEx). Universidade Federal de Minas Gerais (UFMG). Ministério da Educação (Brasil). Belo Horizonte , SP, Brazil
• Associated researchers: Alejandro César Frery Orgambide ; Amir Houmansadr ; Antonio Alfredo Ferreira Loureiro ; Fabricio Murai Ferreira ; Leandro Aparecido Villas
• Associated scholarship(s): 24/13480-5 - Federated Continual learning, BP.TT

Abstract

The research project called Quantifying Uncertainty in Adversarial Federated Learning aims to analyze and propose new approaches to distributed machine learning models that maintain privacy and security restrictions. Federated Learning (FL) is a promising approach to training data collaboratively on distributed devices while accounting for privacy restrictions. However, the FL training process is vulnerable to model poisoning attacks where malicious participants can upload fake model weights. The project aims to address these vulnerabilities and propose new solutions for maintaining privacy and security in distributed machine learning models. In short, this project presents a scientific research proposal in five directions: (i) quantification of model generalization based on Bayesian neural networks for federated learning systems; (ii) DDoS intrusion detection system approaches in federated applications; (iii) uncertainty quantification in distributed heterogeneous environment (e.g., Federated Learning); (iv) investigation for continual (incremental) learning to identify unknown new malware is necessary to protect systems even at day zero of a malware release; and (v) study the use of ordinal patterns statistical tests to identify data poisoning attacks in federated applications. (AU)

Scientific publications

(9)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

MOURA, DOUGLAS L. L.; AQUINO, ANDRE L. L.; LOUREIRO, ANTONIO A. F.. On the Integration of Ledger Technology and Edge Computing for Intelligent Transportation Systems. PROCEEDINGS OF THE INT'L ACM SYMPOSIUM ON PERFORMANCE EVALUATION OF WIRELESS AD HOC, SENSOR, & UBIQUITOUS NETWORKS, PE-WASUN 2023, v. N/A, p. 8-pg., 2023-01-01. (15/24494-8, 23/00721-1)

MOURA, DOUGLAS L. L.; AQUINO, ANDRE L. L.; LOUREIRO, ANTONIO A. F.. An edge computing and distributed ledger technology architecture for secure and efficient transportation. Ad Hoc Networks, v. 164, p. 14-pg., 2024-08-28. (15/24494-8, 23/00721-1)

FIGUEIREDO, LEONARDO J. A. S.; FIGUEIREDO, RAISSA P. P. S.; DOS SANTOS, GERMANO B.; SILVA, FABRICIO A.; SILVA, THAIS R. M. B.; LOUREIRO, ANTONIO A. F.. Extracting Mobile User Profile using Easy-to-obtain and Less Invasive Data. PROCEEDINGS OF THE INT'L ACM SYMPOSIUM ON PERFORMANCE EVALUATION OF WIRELESS AD HOC, SENSOR, & UBIQUITOUS NETWORKS, PE-WASUN 2023, v. N/A, p. 8-pg., 2023-01-01. (15/24494-8, 23/00721-1)

DE MATTOS, EKLER PAULINO; DOMINGUES, AUGUSTO C. S. A.; SILVA, FABRICIO A.; RAMOS, HEITOR S.; LOUREIRO, ANTONIO A. F.. Slicing who slices: Anonymization quality evaluation on deployment, privacy, and utility in mix-zones. Computer Networks, v. 236, p. 19-pg., 2023-09-16. (23/00721-1)

BARROS, PEDRO H.; GUEVARA, JUDY C.; VILLAS, LEANDRO; GUIDONI, DANIEL; DA FONSECA, NELSON L. S.; RAMOS, HEITOR S.. A Novel Federated Meta-Learning Approach for Discriminating Sedentary Behavior From Wearable Data. IEEE INTERNET OF THINGS JOURNAL, v. 11, n. 19, p. 8-pg., 2024-10-01. (23/00721-1)

ORANG, OMID; DA SILVA, FELIPE A. R.; SILVA, PETRONIO C. L.; BARROS, PEDRO H. S. S.; RAMOS, HEITOR S.; GUIMARAES, FREDERICO G.. Traffic Forecasting Using Federated Randomized High-Order Fuzzy Cognitive Maps. INTELLIGENT SYSTEMS, BRACIS 2024, PT II, v. 15413, p. 15-pg., 2025-01-01. (23/00721-1)

BINE, LAILLA M. S.; BOUKERCHE, AZZEDINE; RUIZ, LINNYER B.; LOUREIRO, ANTONIO A. F.. Drone Delivery: Why, Where, and When. PROCEEDINGS OF THE INT'L ACM SYMPOSIUM ON PERFORMANCE EVALUATION OF WIRELESS AD HOC, SENSOR, & UBIQUITOUS NETWORKS, PE-WASUN 2023, v. N/A, p. 9-pg., 2023-01-01. (15/24494-8, 23/00721-1)

VIANA, CAIO M. C.; FERREIRA, CARLOS H. G.; MURAI, FABRICIO; DOS SANTOS, ALDRI LUIZ; PEREIRA, LOURENCO ALVES, JR.. Devil in the Noise: Detecting Advanced Persistent Threats with Backbone Extraction. 2024 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS, ISCC 2024, v. N/A, p. 7-pg., 2024-01-01. (22/00741-0, 23/00721-1, 20/09850-0)

DE MATTOS, EKLER PAULINO; DOMINGUES, AUGUSTO C. S. A.; SILVA, FABRICIO A.; RAMOS, HEITOR S.; LOUREIRO, ANTONIO A. F.. Protect your Data and I'll Show Its Utility: A Practical View about Mix-zones Impacts on Mobility Data for Smart City Applications. PROCEEDINGS OF THE INT'L ACM SYMPOSIUM ON PERFORMANCE EVALUATION OF WIRELESS AD HOC, SENSOR, & UBIQUITOUS NETWORKS, PE-WASUN 2023, v. N/A, p. 8-pg., 2023-01-01. (15/24494-8, 23/00721-1)