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Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities

117
Citations
March 3, 2025
Published Date

Research Abstract & Technology Focus

The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and artificial intelligence (AI) to improve cloud computing security while preserving privacy, delivering real-time threat detection, and meeting regulatory requirements. The current research follows a systematic literature review (SLR) approach, which examined 30 studies published between 2020 and 2024 and followed the PRISMA 2020 checklist. The analysis shows that FL provides significant privacy risk reduction by 25%, especially in healthcare and similar domains, and it improves threat detection by 40% in critical infrastructure areas. A total of 80% of reviewed implementations showed improved privacy, but challenges like communication overhead and resource limitations persist, with 50% of studies reporting latency issues. To overcome these obstacles, this study also explores some emerging solutions, which include model compression, hybrid federated architectures, and cryptographic enhancements. Additionally, this paper demonstrates the unexploited capability of FL for real-time decision-making in dynamic edge environments and highlights its potential across autonomous systems, Industrial Internet of Things (IIoT), and cybersecurity frameworks. The paper’s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era.
federated learning cloud security systematic review
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Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities

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Frequently Asked Questions (FAQ)

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What is the core focus of the research titled 'Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities'?

This literature focuses on: The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) an...

Are there open-source GitHub repositories related to Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities?

Yes, open-source projects like RunanywhereAI/RCLI (Talk to your Mac, query your docs, no cloud required. On-device voice AI + RAG) are actively building upon these concepts.

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What other academic literature is closely related to 'Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities'?

Yes, highly correlated activity was mapped. An entry titled 'Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities' discusses this: The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to ...

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    RunanywhereAI/RCLI
    Talk to your Mac, query your docs, no cloud required. On-device voi...
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