Federated Learning: Attacks, Defenses, Opportunities, and Challenges

TL;DR

This paper provides a comprehensive overview of federated learning's security and privacy challenges, analyzing attack surfaces, defenses, and future research directions to facilitate broader adoption.

Contribution

It offers a detailed analysis of FL's attack vectors, defenses, and identifies key security threats, bridging the gap between current research and practical deployment.

Findings

Security concerns are more prevalent than privacy issues in FL.

Communication bottlenecks, poisoning, and backdoor attacks are major threats.

Future research directions are outlined for real-world adaptation.

Abstract

Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the community's trust since its security and privacy implications are controversial. FL's security and privacy concerns must be discovered, analyzed, and recorded before widespread usage and adoption. A solid comprehension of risk variables allows an FL practitioner to construct a secure environment and provide researchers with a clear perspective of potential study fields, making FL the best solution in situations where security and privacy are primary issues. This research aims to deliver a complete overview of FL's security and privacy features to help bridge the gap between current federated AI and broad adoption in the future. In this paper, we present a comprehensive overview of the attack surface to investigate FL's existing challenges and defense measures to evaluate its robustness and reliability. According to our study, security concerns regarding FL are more frequent than privacy issues. Communication bottlenecks, poisoning, and backdoor attacks represent FL's privacy's most significant security threats. In the final part, we detail future research that will assist FL in adapting to real-world settings.