Navigating the Void: Uncovering Research Gaps in the Detection of Data Poisoning Attacks in Federated Learning-Based Big Data Processing: A Systematic Literature Review

Abstract

This systematic literature review scrutinizes the landscape of research at the intersection of federated learning, big data processing, and data poisoning attacks. Employing a meticulous search strategy across multiple databases, the study unveils a surge in annual scientific production, emphasizing a growing interest in federated learning and related fields. However, a critical research gap becomes evident during the investigation of data poisoning attacks specifically in the context of federated learning when processing big data. The most relevant keywords and a visually compelling word cloud further illuminate the prevailing themes and emphases within the literature, emphasizing the lack of explicit focus on detecting data poisoning attacks. This identified gap presents a significant avenue for future research, offering opportunities to enhance the security and robustness of federated learning systems against adversarial threats in large-scale data scenarios.