Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions

Tuan Minh Nguyen

Abstract:

Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data
without compromising personal privacy. However, the heterogeneous distribution of data among
clients in FL can make it difficult for the orchestration server to validate the integrity of local model
updates, making FL vulnerable to various threats, including backdoor attacks. Backdoor attacks
involve the insertion of malicious functionality into a targeted model through poisoned updates
from malicious clients. These attacks can cause the global model to misbehave on specific inputs
while appearing normal in other cases. Backdoor attacks have received significant attention in the
literature due to their potential to impact real-world deep learning applications. However, they
have not been thoroughly studied in the context of FL. In this survey, we provide a comprehensive survey of current backdoor attack strategies and defenses in FL, including a comprehensive
analysis of different approaches. We also discuss the challenges and potential future directions for
attacks and defenses in the context of FL.