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Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses-A Survey.

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Federated Learning (FL) with Edge Computing (EC) offers privacy and efficiency but faces security risks. This survey reviews vulnerabilities and defenses for robust FL in edge environments.

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Area of Science:

  • Distributed Machine Learning
  • Edge Computing
  • Cybersecurity

Background:

  • Federated Learning (FL) enables collaborative training without raw data sharing, enhancing privacy and reducing communication overhead.
  • Integrating FL with Edge Computing (EC) facilitates real-time, low-latency decisions in resource-limited settings by moving computation closer to data.
  • Decentralization in FL and EC introduces significant vulnerabilities like data poisoning, backdoor attacks, inference leaks, and Byzantine behaviors, exacerbated by device heterogeneity and connectivity issues.

Purpose of the Study:

  • To provide a comprehensive review of the intersection of Federated Learning and Edge Computing.
  • To focus on identifying and analyzing vulnerabilities, attack vectors, and defense mechanisms pertinent to FL in EC environments.
  • To inform future research directions for secure, private, and efficient FL systems in real-world edge deployments.

Main Methods:

  • Systematic review of existing literature on Federated Learning and Edge Computing.
  • Analysis of current defense strategies, including robust aggregation, anomaly detection, differential privacy, and secure aggregation.
  • Evaluation of the feasibility and applicability of these defenses within the constraints of edge environments.

Main Results:

  • Identified key vulnerabilities and attack vectors inherent to decentralized FL and EC systems.
  • Analyzed the effectiveness and limitations of existing defense mechanisms in addressing these threats.
  • Highlighted the challenges posed by device heterogeneity and intermittent connectivity in edge deployments.

Conclusions:

  • The combination of FL and EC presents a promising paradigm but requires robust security and privacy measures.
  • Existing defenses show potential but need adaptation for the unique challenges of edge environments.
  • Further research is crucial for developing scalable, resilient, and energy-efficient solutions for secure FL in EC.