A Privacy Preservation Framework Using Integration of Blockchain and Federated Learning. - K. M. Sameera; Rafidha Rehiman K. A.; P. Vinod 001 (SN Comput. Sci., 2023)

A Privacy-Preserving Byzantine-Aware Swarm Learning Framework for CTI. - Marco Arazzi; Dincy R. Arikkat; Mert Cihangiroglu; K. M. Sameera (ACSAC Workshops, 2024)

DLShield: A Defense Approach Against Dirty Label Attacks in Heterogeneous Federated Learning. - K. M. Sameera; M. Abhinav; P. P. Amal; T. Babu Abhiram; Raj K. Abishek; Tomichen Amal; P. Anaina; P. Vinod 001; Rafidha Rehiman K. A.; Mauro Conti (SPACE, 2024)

LFGurad: A Defense against Label Flipping Attack in Federated Learning for Vehicular Network. - K. M. Sameera; Vinod P. 001; Rafidha Rehiman K. A.; Mauro Conti (Comput. Networks, 2024)

Privacy-preserving in Blockchain-based Federated Learning systems. - K. M. Sameera; Serena Nicolazzo; Marco Arazzi; Antonino Nocera; Rafidha Rehiman K. A.; Vinod P. 001; Mauro Conti (Comput. Commun., 2024)

SecDefender: Detecting low-quality models in multidomain federated learning systems. - K. M. Sameera; Arnaldo Sgueglia; P. Vinod 001; Rafidha Rehiman K. A.; Corrado Aaron Visaggio; Andrea Di Sorbo; Mauro Conti (Future Gener. Comput. Syst., 2025)

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems. - K. M. Sameera; P. Vinod 001; Anderson Rocha 001; Rafidha Rehiman K. A.; Mauro Conti (CoRR, 2025)