Navigating the Void: Uncovering Research Gaps in the Detection of Data Poisoning Attacks in Federated Learning-Based Big Data Processing: A Systematic Literature Review
Mohammad Aljanabi, Hijaz Ahmad
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.