Standing Firm in 5G: A Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning

• URL: http://arxiv.org/abs/2505.07148v1
• Date: Sun, 11 May 2025 23:37:07 GMT
• Title: Standing Firm in 5G: A Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning
• Authors: Yiwei Zhang, Rouzbeh Behnia, Imtiaz Karim, Attila A. Yavuz, Elisa Bertino,
• Abstract summary: Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data.<n>Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data.<n>We propose a lightweight, single-round secure aggregation protocol designed for 5G environments.
• Score: 19.014890294716043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data. However, the dynamic and large-scale nature of 5G-marked by high mobility and frequent dropouts-poses significant challenges to the effective adoption of these protocols. Existing protocols often require multi-round communication or rely on fixed infrastructure, limiting their practicality. We propose a lightweight, single-round secure aggregation protocol designed for 5G environments. By leveraging base stations for assisted computation and incorporating precomputation, key-homomorphic pseudorandom functions, and t-out-of-k secret sharing, our protocol ensures efficiency, robustness, and privacy. Experiments show strong security guarantees and significant gains in communication and computation efficiency, making the approach well-suited for real-world 5G FL deployments.

Related papers

• PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts [59.5243730853157]
  Large language models (LLMs) hosted on cloud servers alleviate the computational and storage burdens on local devices but raise privacy concerns.<n>Small language models (SLMs) running locally enhance privacy but suffer from limited performance on complex tasks.<n>We propose a privacy-aware wireless collaborative mixture of experts (PWC-MoE) framework to balance computational cost, performance, and privacy protection under bandwidth constraints.
  arXiv  Detail & Related papers  (2025-05-13T16:27:07Z)
• Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security [17.45950557331482]
  Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local.<n>Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment.<n>We introduce Beskar, a novel framework that provides post-quantum secure aggregation.
  arXiv  Detail & Related papers  (2025-05-09T03:20:48Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data. The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• UniHand: Privacy-preserving Universal Handover for Small-Cell Networks in 5G-enabled Mobile Communication with KCI Resilience [7.816521719452984]
  Introducing Small Cell Networks (SCN) has significantly improved wireless link quality, spectrum efficiency and network capacity. This work proposes a secure privacy-preserving universal HO scheme ($UniHand$) for SCNs in 5G mobile communication.
  arXiv  Detail & Related papers  (2024-03-12T16:56:31Z)
• Blockchain-Enhanced UAV Networks for Post-Disaster Communication: A Decentralized Flocking Approach [7.249638174814088]
  Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios. efficiently coordinating and securing flocks of heterogeneous UAVs from different service providers poses significant challenges related to privacy, scalability, lightweight consensus protocols, and comprehensive cybersecurity mechanisms. This study introduces a robust blockchain-enabled framework designed to tackle these technical challenges through a combination of consensus protocols, smart contracts, and cryptographic techniques.
  arXiv  Detail & Related papers  (2024-03-05T00:59:23Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification [65.7405397206767]
  We propose Federated Deep-Broad Learning (FedDBL) to achieve superior classification performance with limited training samples and only one-round communication. FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk.
  arXiv  Detail & Related papers  (2023-02-24T14:27:41Z)
• Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT [57.67687126339891]
  FedGS is a hierarchical cloud-edge-end FL framework for 5G empowered industries. Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm. Experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average.
  arXiv  Detail & Related papers  (2022-02-03T10:48:17Z)
• To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design [49.95746344960136]
  Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. It is not clear how to establish an effective wireless network architecture to support FL over mobile devices. We develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices.
  arXiv  Detail & Related papers  (2020-12-21T01:13:44Z)
• Secure Federated Learning in 5G Mobile Networks [2.4366811507669124]
  We integrate Federated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA) architecture. We add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower overhead than previous work, without affecting ML performance.
  arXiv  Detail & Related papers  (2020-04-14T17:53:33Z)
• 5G Security and Privacy: A Research Roadmap [24.802753928579477]
  5G - the latest generation of cellular networks - combines different technologies to increase capacity, reduce latency, and save energy. We outline recent approaches supporting systematic analyses of 4G LTE and 5G protocols and their related defenses.
  arXiv  Detail & Related papers  (2020-03-30T16:36:43Z)
• Experimental quantum conference key agreement [55.41644538483948]
  Quantum networks will provide multi-node entanglement over long distances to enable secure communication on a global scale. Here we demonstrate quantum conference key agreement, a quantum communication protocol that exploits multi-partite entanglement. We distribute four-photon Greenberger-Horne-Zeilinger (GHZ) states generated by high-brightness, telecom photon-pair sources across up to 50 km of fibre.
  arXiv  Detail & Related papers  (2020-02-04T19:00:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.