mlsanityData to 14 Oct 2026

Emerging Paradigms for Securing Federated Learning Systems

arXiv 2509.21147 ↗First posted 25 Sep 2025cs.CR cs.AI cs.ETcited by 0

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Abstract (arXiv)

Federated Learning (FL) facilitates collaborative model training while keeping raw data decentralized, making it a conduit for leveraging the power of IoT devices while maintaining privacy of the locally collected data. However, existing privacy- preserving techniques present notable hurdles. Methods such as Multi-Party Computation (MPC), Homomorphic Encryption (HE), and Differential Privacy (DP) often incur high compu- tational costs and suffer from limited scalability. This survey examines emerging approaches that hold promise for enhancing both privacy and efficiency in FL, including Trusted Execution Environments (TEEs), Physical Unclonable Functions (PUFs), Quantum Computing (QC), Chaos-Based Encryption (CBE), Neuromorphic Computing (NC), and Swarm Intelligence (SI). For each paradigm, we assess its relevance to the FL pipeline, outlining its strengths, limitations, and practical considerations. We conclude by highlighting open challenges and prospective research avenues, offering a detailed roadmap for advancing secure and scalable FL systems.

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References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  3. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  4. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  5. 2021Federated Quantum Machine Learningcited by 82
  6. 2021Quantum Federated Learning with Quantum Datacited by 53
  7. 2021GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learningcited by 36
  8. 2023Privacy-preserving quantum federated learning via gradient hidingcited by 15
  9. 2023Blind quantum machine learning with quantum bipartite correlatorcited by 8
  10. 2023Quantum Federated Learning: Analysis, Design and Implementation Challengescited by 6
  11. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  12. 2024A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasetscited by 3
4 more
  1. 2022Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networkscited by 2
  2. 2022Proof of Swarm Based Ensemble Learning for Federated Learning Applicationscited by 1
  3. 2024Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applicationscited by 1
  4. 2025A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environmentscited by 1

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