On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
2025-08-20
Summary
The article provides a comprehensive survey of federated learning (FL), focusing on security and privacy aspects. It categorizes and analyzes over 200 papers on attacks such as poisoning and backdoor, and defenses like robust aggregation and differential privacy. The survey also examines the impact of non-IID data, evaluates frameworks, and suggests future research directions.
Why This Matters
Federated learning is increasingly used in sensitive domains like healthcare and finance, where data privacy and security are paramount. Understanding the vulnerabilities and defenses in FL helps organizations mitigate risks, ensuring data integrity and confidentiality while benefiting from collaborative learning.
How You Can Use This Info
Professionals can leverage the insights from this survey to enhance the security and privacy of their FL implementations by applying recommended defense mechanisms. Additionally, understanding potential attack vectors allows for better risk assessment and the development of robust, privacy-preserving FL systems.