Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation

• URL: http://arxiv.org/abs/2403.04803v1
• Date: Tue, 5 Mar 2024 20:54:56 GMT
• Title: Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation
• Authors: Zahir Alsulaimawi
• Abstract summary: This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a consensus-based verification process integrated with an adaptive thresholding mechanism. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
• Score: 2.28438857884398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.

Related papers

• Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings [0.24578723416255752]
  We develop scalable formal verification algorithms without shifting the problem to unrealistic assumptions. In a pursuit of developing scalable formal verification algorithms without shifting the problem to unrealistic assumptions, we employ the concept of barrier certificates. We show how to solve the resulting program efficiently using sum-of-squares optimization and a Gaussian process envelope.
  arXiv  Detail & Related papers  (2024-03-15T17:32:02Z)
• Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks [2.28438857884398]
  Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges. This study proposes an innovative security framework inspired by Control-Flow (CFA) mechanisms, traditionally used in cybersecurity. We authenticate and verify the integrity of model updates across the network, effectively mitigating risks associated with model poisoning and adversarial interference.
  arXiv  Detail & Related papers  (2024-03-15T04:03:34Z)
• Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis [2.28438857884398]
  We introduce a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision. Our method outperforms existing solutions by 15% in anomaly detection accuracy while maintaining a minimal false positive rate. Our work paves the way for future advancements in distributed learning security.
  arXiv  Detail & Related papers  (2024-03-15T03:54:45Z)
• Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions [6.318638597489423]
  Reinforcement Federated Learning (RFL) is a novel framework that leverages deep reinforcement learning to adaptively optimize client contribution during aggregation. In terms of robustness, RFL outperforms state-of-the-art methods, while maintaining comparable levels of fairness.
  arXiv  Detail & Related papers  (2024-02-08T10:22:12Z)
• Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications [5.5099914877576985]
  Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance. This paper introduces an innovative solution named Estimated Mean Aggregation (EMA) that not only addresses these challenges but also provides a fundamental reference point as a $mathsfbaseline$ for advanced aggregation techniques in FL systems.
  arXiv  Detail & Related papers  (2023-09-21T17:17:28Z)
• Conformal Prediction for Federated Uncertainty Quantification Under Label Shift [57.54977668978613]
  Federated Learning (FL) is a machine learning framework where many clients collaboratively train models. We develop a new conformal prediction method based on quantile regression and take into account privacy constraints.
  arXiv  Detail & Related papers  (2023-06-08T11:54:58Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [63.18590014127461]
  This paper introduces a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We study the feasibility of the resulting robust safety-critical controller. We then use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• A Regularized Implicit Policy for Offline Reinforcement Learning [54.7427227775581]
  offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. We propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. Experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
  arXiv  Detail & Related papers  (2022-02-19T20:22:04Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• 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)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)

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.