HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (1912.05897v1)

Abstract: Federated learning has emerged as a promising approach for collaborative and privacy-preserving learning. Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private. However, parameter interaction and the resulting model still might disclose information about the training data used. To address these privacy concerns, several approaches have been proposed based on differential privacy and secure multiparty computation (SMC), among others. They often result in large communication overhead and slow training time. In this paper, we propose HybridAlpha, an approach for privacy-preserving federated learning employing an SMC protocol based on functional encryption. This protocol is simple, efficient and resilient to participants dropping out. We evaluate our approach regarding the training time and data volume exchanged using a federated learning process to train a CNN on the MNIST data set. Evaluation against existing crypto-based SMC solutions shows that HybridAlpha can reduce the training time by 68% and data transfer volume by 92% on average while providing the same model performance and privacy guarantees as the existing solutions.

• The paper introduces a novel functional encryption-based secure multiparty computation protocol for efficient, privacy-preserving federated learning.
• It significantly reduces training time by 68% and communication overhead by 92% without compromising model performance.
• The framework supports dynamic participation and prevents inference attacks, enhancing robustness in privacy-sensitive settings.

Overview of "HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning"

The paper "HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning" by Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig presents a novel framework called HybridAlpha aimed at enhancing the efficiency and privacy of federated learning (FL). Federated learning is a collaborative machine learning approach where the model training is conducted across multiple decentralized devices while keeping the data localized and secure. The primary focus of this paper is to address the challenge of ensuring privacy in FL while minimizing communication overhead and training time.

Key Contributions

1. Functional Encryption-based SMC Protocol: The paper introduces an innovative use of functional encryption to devise a secure multiparty computation (SMC) protocol. This protocol provides privacy guarantees similar to existing methods but with improved efficiency. Unlike traditional encryption systems used in FL, this protocol facilitates reduced communication rounds and efficient computation.
2. Reduction in Training Time and Communication Overhead: Through their experimental evaluation, the authors demonstrate a significant reduction in training time by 68% and data transfer volume by 92% on average when compared with existing cryptographic SMC solutions. These results are achieved without sacrificing the model's performance or privacy guarantees.
3. Dynamic Participation and Dropout Resilience: HybridAlpha is designed to accommodate the addition or dropout of participants during the training phase dynamically. This is achieved through the novel use of functional encryption, enhancing the robustness and flexibility of the federated learning process in real-world applications.
4. Inference Prevention Module: The framework includes a module to mitigate potential inference attacks from curious aggregators or colluding participants. This module ensures that the functional encryption keys are only generated for legitimate aggregation requests, preventing the unauthorized inference of private data.

Implications and Future Directions

The HybridAlpha framework has significant implications for industries requiring stringent privacy preservation, such as healthcare and finance. By enabling efficient and robust federated learning, the framework ensures compliance with privacy regulations like GDPR and HIPAA, providing a viable solution for privacy-sensitive applications.

Theoretically, this work expands the utility of functional encryption within the FL paradigm, suggesting its potential applicability in other distributed learning systems. The experimental results indicate superiority in efficiency, which could pave the way for further exploration of efficient cryptographic methods in federated environments.

Future research could focus on extending HybridAlpha to vertical federated learning scenarios, enhancing the framework's adaptability to different data partition strategies. Additionally, integrating more advanced privacy-preserving mechanisms, such as zero-knowledge proofs, with the existing framework could further bolster security while maintaining efficiency.