MONZA: A Score System for Malicious Clients Detection

Abstract

Federated Learning (FL) is a machine learning training method that uses a collaborative model for training across a diverse set of clients, while preserving data privacy in accordance with the General Data Protection Regulation (GDPR) for clients' data. The classification and similarity within many clients can become an issue, often leading to decreased model accuracy and slower convergence, or reducing the number of parameters to the point of not learning anything during aggregation. However, the presence of non-IID data and malicious clients poses significant challenges to the significance of generalization models and distribution data. Malicious clients can perform poisoning attacks by sending harmful model updates that degrade the performance of the global model. This article introduces MONZA, a scoring system designed to detect and exclude malicious clients in FL environments. In a scenario where clients can also engage in various attacks, including model poisoning and data poisoning, this can lead to incorrect training. The proposed method uses cosine similarity to calculate client scores. It employs L2 normalization to identify biased models; in some cases, the similarity is not sufficient to classify a client, effectively filtering out malicious participants before aggregation and implementing a penalty with a quarentine method. Our evaluation shows that MONZA achieves an accuracy of 54.5% in a scenario with 30% malicious clients, while zPROBE (i.e., existing resilient methods) only reached an accuracy of 50%. Furthermore, MONZA reduces the simulation execution time by 66% and the computational effort to 83 MFLOP/s compared to zPROBE, which demonstrates to be a more efficient and resilient method. These results confirm that MONZA maintains the integrity of the model, making a security aggregation while minimizing resource consumption in malicious FL settings.