Inverting Gradients -- How easy is it to break privacy in federated learning? (2003.14053v2)

Abstract: The idea of federated learning is to collaboratively train a neural network on a server. Each user receives the current weights of the network and in turns sends parameter updates (gradients) based on local data. This protocol has been designed not only to train neural networks data-efficiently, but also to provide privacy benefits for users, as their input data remains on device and only parameter gradients are shared. But how secure is sharing parameter gradients? Previous attacks have provided a false sense of security, by succeeding only in contrived settings - even for a single image. However, by exploiting a magnitude-invariant loss along with optimization strategies based on adversarial attacks, we show that is is actually possible to faithfully reconstruct images at high resolution from the knowledge of their parameter gradients, and demonstrate that such a break of privacy is possible even for trained deep networks. We analyze the effects of architecture as well as parameters on the difficulty of reconstructing an input image and prove that any input to a fully connected layer can be reconstructed analytically independent of the remaining architecture. Finally we discuss settings encountered in practice and show that even averaging gradients over several iterations or several images does not protect the user's privacy in federated learning applications in computer vision.

• The paper introduces an improved gradient inversion attack using a magnitude-invariant loss and Adam optimizer for enhanced data reconstruction.
• It demonstrates that detailed images can be reconstructed from gradients, even in deep architectures and federated averaging scenarios.
• The authors provide analytical proofs for reconstructing inputs from fully connected layers, emphasizing the need for stronger FL defenses.

Inverting Gradients: How Easy is it to Break Privacy in Federated Learning?

Introduction

The paper "Inverting Gradients - How easy is it to break privacy in federated learning?" by Jonas Geiping et al. offers a comprehensive exploration of the vulnerability of federated learning (FL) to privacy attacks by inverting parameter gradients. FL is a technique that allows multiple users to cooperatively train a neural network without sharing raw data, ostensibly protecting user privacy by only exchanging parameter gradients. However, the security of this paradigm is challenged by the possibility of reconstructing original data from its gradients.

Main Contributions and Findings

1. Improved Attack Mechanism:
   • The authors leverage a magnitude-invariant loss and optimization strategies inspired by adversarial attacks.
   • They minimize cosine similarity rather than Euclidean distance for gradient reconstruction.
   • The paper argues for using Adam optimizer with signed gradients, contrasting prior approaches based on L-BFGS.
2. Capability to Reconstruct Data:
   • The paper demonstrates that detailed images can be reconstructed from parameter gradients, even for high-resolution datasets such as ImageNet.
   • It empirically shows that this reconstruction is feasible for deep, non-smooth architectures like ResNet.
3. Analytical Reconstruction:
   • The authors provide theoretical results proving that any input to a fully connected layer can be reconstructed analytically from a parameter gradient, independently of the preceding network architecture.
   • This theoretical insight highlights that privacy breaches are not limited to simple, contrived settings but apply broadly to practical network architectures used in FL.
4. Robustness Against Averaging and Deep Networks:
   • The paper tests gradient inversion attacks in federated averaging setups, which aggregate gradients over multiple local iterations or mini-batches.
   • It shows that averaging gradients between multiple images does not inherently guarantee privacy. Even batches as large as 100 images contain several recoverable images.

Experimental Insights

1. Comparison with Previous Works:
   • The authors compare their method with earlier gradient inversion approaches that use the Euclidean loss optimized through L-BFGS. They find that the proposed method achieves superior reconstruction quality across different network architectures and training statuses.
   • Experiments on CIFAR-10 and ImageNet validate the robustness of their approach in practical scenarios, demonstrating significant improvements in recovering private data.
2. Impact of Network Architecture:
   • The robustness of the proposed method is tested against variations in network width and depth. Wider networks show higher average PSNR, indicating some robustness against gradient inversion due to increased computational effort.
   • Deep networks, such as ResNet-50 and ResNet-152, also show vulnerability to the proposed attack, albeit with some loss in the reconstruction fidelity due to deeper layers.
3. Handling Federated Averaging:
   • The paper investigates the impact of federated averaging on data privacy and finds that privacy is not guaranteed even when aggregating over multiple gradient steps or large batches. This is a critical real-world scenario where users might assume added privacy through gradient averaging.
   • By averaging over multiple images and local iterations, the authors show varying degrees of success in reconstructing the input images, underscoring the persistent privacy risks in FL.

Practical and Theoretical Implications

The findings of this paper are essential for both theoreticians and practitioners. They underscore that federated learning, while promising for privacy preservation, still falls short when scrutinized under gradient inversion attacks. The paper argues for more robust defense mechanisms, including differential privacy and secure computation protocols, as integral to the practical deployment of FL systems.

Future Directions

The paper opens several avenues for future work:

• Enhanced Defensive Mechanisms:
  • Develop and test novel defense strategies like differential privacy that can provide provable security guarantees in federated learning contexts.
• In-depth Analysis of Other Data Modalities:
  • Extend the gradient inversion analysis to other data types like text and tabular data to evaluate their susceptibility and develop domain-specific defenses.
• Optimization of Privacy-Accuracy Trade-offs:
  • Investigate the balance between privacy retention and model accuracy, particularly under rigorous privacy-preserving methods such as secure aggregation.

This paper forms a significant contribution to the literature by analytically and empirically detailing vulnerabilities in FL models, highlighting an urgent need for comprehensive privacy-preserving techniques beyond the conventional federated setup.