FuSeFL: Fully Secure and Scalable Cross-Silo Federated Learning

• URL: http://arxiv.org/abs/2507.13591v1
• Date: Fri, 18 Jul 2025 00:50:44 GMT
• Title: FuSeFL: Fully Secure and Scalable Cross-Silo Federated Learning
• Authors: Sahar Ghoflsaz Ghinani, Elaheh Sadredini,
• Abstract summary: Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains.<n>We present FuSeFL, a fully secure and scalable FL scheme designed for cross-silo settings.
• Score: 0.8686220240511062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains. While existing approaches employ cryptographic techniques such as homomorphic encryption, differential privacy, or secure multiparty computation to mitigate inference attacks-including model inversion, membership inference, and gradient leakage-they often suffer from high computational, communication, or memory overheads. Moreover, many methods overlook the confidentiality of the global model itself, which may be proprietary and sensitive. These challenges limit the practicality of secure FL, especially in cross-silo deployments involving large datasets and strict compliance requirements. We present FuSeFL, a fully secure and scalable FL scheme designed for cross-silo settings. FuSeFL decentralizes training across client pairs using lightweight secure multiparty computation (MPC), while confining the server's role to secure aggregation. This design eliminates server bottlenecks, avoids data offloading, and preserves full confidentiality of data, model, and updates throughout training. FuSeFL defends against inference threats, achieves up to 95% lower communication latency and 50% lower server memory usage, and improves accuracy over prior secure FL solutions, demonstrating strong security and efficiency at scale.

Related papers

• FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model [0.48342038441006796]
  Federated Learning (FL) offers a decentralized framework for training and fine-tuning Large Language Models (LLMs)<n>FL addresses privacy and security concerns while navigating challenges associated with the substantial computational demands of LLMs.<n>We propose a novel method, FedShield-LLM, that uses pruning with Fully Homomorphic Encryption (FHE) for Low-Rank Adaptation (LoRA) parameters.
  arXiv  Detail & Related papers  (2025-06-06T00:05:05Z)
• RLSA-PFL: Robust Lightweight Secure Aggregation with Model Inconsistency Detection in Privacy-Preserving Federated Learning [12.804623314091508]
  Federated Learning (FL) allows users to collaboratively train a global machine learning model by sharing local model only, without exposing their private data to a central server.<n>Study have revealed privacy vulnerabilities in FL, where adversaries can potentially infer sensitive information from the shared model parameters.<n>We present an efficient masking-based secure aggregation scheme utilizing lightweight cryptographic primitives to privacy risks.
  arXiv  Detail & Related papers  (2025-02-13T06:01:09Z)
• BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption [0.0]
  Federated learning (FL) is a privacy-preserving edge-to-cloud technique used for training and deploying AI models on edge devices.<n>BlindFL is a framework for global model aggregation in which clients encrypt and send a subset of their local model update.<n>BlindFL significantly impedes client-side model poisoning attacks, a first for single-key, FHE-based FL schemes.
  arXiv  Detail & Related papers  (2025-01-20T18:42:21Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters [3.9660142560142067]
  Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. FL remains vulnerable to inference attacks during model update transmissions. We present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding.
  arXiv  Detail & Related papers  (2024-06-13T14:16:50Z)
• Personalized Wireless Federated Learning for Large Language Models [75.22457544349668]
  Large language models (LLMs) have driven profound transformations in wireless networks.<n>Within wireless environments, the training of LLMs faces significant challenges related to security and privacy.<n>This paper presents a systematic analysis of the training stages of LLMs in wireless networks, including pre-training, instruction tuning, and alignment tuning.
  arXiv  Detail & Related papers  (2024-04-20T02:30:21Z)
• Mitigating Cross-client GANs-based Attack in Federated Learning [78.06700142712353]
  Multi distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model. FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack. We propose Fed-EDKD technique to improve the current popular FL schemes to resist C-GANs attack.
  arXiv  Detail & Related papers  (2023-07-25T08:15:55Z)
• FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users [19.209830150036254]
  federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. Next-generation FL architectures proposed encryption and anonymization techniques to protect the model updates from the server. This paper proposes a novel FL algorithm based on a fully homomorphic encryption (FHE) scheme.
  arXiv  Detail & Related papers  (2023-06-08T11:20:00Z)
• WW-FL: Secure and Private Large-Scale Federated Learning [15.412475066687723]
  Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices. Recent research has uncovered vulnerabilities in FL, impacting both security and privacy through poisoning attacks. We propose WW-FL, an innovative framework that combines secure multi-party computation with hierarchical FL to guarantee data and global model privacy.
  arXiv  Detail & Related papers  (2023-02-20T11:02:55Z)
• ScionFL: Efficient and Robust Secure Quantized Aggregation [36.668162197302365]
  We introduce ScionFL, the first secure aggregation framework for federated learning. It operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients. We show that with no overhead for clients and moderate overhead for the server, we obtain comparable accuracy for standard FL benchmarks.
  arXiv  Detail & Related papers  (2022-10-13T21:46:55Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)

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.