LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation

• URL: http://arxiv.org/abs/2502.04601v1
• Date: Fri, 07 Feb 2025 01:21:37 GMT
• Title: LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
• Authors: Abhinav Kumar, George Torres, Noah Guzinski, Gaurav Panwar, Reza Tourani, Satyajayant Misra, Marcin Spoczynski, Mona Vij, Nageen Himayat,
• Abstract summary: We propose a privacy-preserving framework that combines a gradient obfuscation mechanism with Trusted Execution Environments (TEEs) for secure asynchronous FL aggregation at the network edge. Our mechanism enables clients to implicitly verify TEE-based aggregation services, effectively handle on-demand client participation, and scale seamlessly with an increasing number of asynchronous connections.
• Score: 6.691450146654845
• License:
• Abstract: The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless, these solutions have predominantly been crafted for and assessed in the context of synchronous FL systems, with minimal focus on asynchronous FL. This gap arises in part due to the unique challenges posed by the asynchronous setting, such as the lack of coordinated updates, increased variability in client participation, and the potential for more severe privacy risks. These concerns have stymied the adoption of asynchronous FL. In this work, we first demonstrate the privacy vulnerabilities of asynchronous FL through a novel data reconstruction attack that exploits gradient updates to recover sensitive client data. To address these vulnerabilities, we propose a privacy-preserving framework that combines a gradient obfuscation mechanism with Trusted Execution Environments (TEEs) for secure asynchronous FL aggregation at the network edge. To overcome the limitations of conventional enclave attestation, we introduce a novel data-centric attestation mechanism based on Multi-Authority Attribute-Based Encryption. This mechanism enables clients to implicitly verify TEE-based aggregation services, effectively handle on-demand client participation, and scale seamlessly with an increasing number of asynchronous connections. Our gradient obfuscation mechanism reduces the structural similarity index of data reconstruction by 85% and increases reconstruction error by 400%, while our framework improves attestation efficiency by lowering average latency by up to 1500% compared to RA-TLS, without additional overhead.

Related papers

• Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective [65.65471972217814]
  federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL. In this paper, we reformulate the Byzantine robustness under sparse aggregation by defining the aggregation for a single item as the smallest execution unit. We propose a family of effective attack strategies, named Spattack, which exploit the vulnerability in sparse aggregation and are categorized along the adversary's knowledge and capability.
  arXiv  Detail & Related papers  (2025-01-06T15:19:26Z)
• 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)
• FedCAP: Robust Federated Learning via Customized Aggregation and Personalization [13.17735010891312]
  Federated learning (FL) has been applied to various privacy-preserving scenarios. We propose FedCAP, a robust FL framework against both data heterogeneity and Byzantine attacks. We show that FedCAP performs well in several non-IID settings and shows strong robustness under a series of poisoning attacks.
  arXiv  Detail & Related papers  (2024-10-16T23:01:22Z)
• A Novel Buffered Federated Learning Framework for Privacy-Driven Anomaly Detection in IIoT [11.127334284392676]
  We propose a Buffered FL (BFL) framework empowered by homomorphic encryption for anomaly detection in heterogeneous IIoT environments. BFL utilizes a novel weighted average time approach to mitigate both straggler effects and communication bottlenecks. Results show the superiority of BFL compared to state-of-the-art FL methods, demonstrating improved accuracy and convergence speed.
  arXiv  Detail & Related papers  (2024-08-16T13:01:59Z)
• 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)
• Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
  We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation. We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
  arXiv  Detail & Related papers  (2023-11-03T13:34:44Z)
• 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)
• FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination [1.0621485365427565]
  This paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination. Experiments on the proposed framework using publicly available industrial control systems datasets reveal superior attack detection accuracy whilst preserving data confidentiality and minimizing the adverse effects of communication latency and stragglers.
  arXiv  Detail & Related papers  (2023-03-28T13:34:38Z)
• 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)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)
• 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.