Parallel Collaborative ADMM Privacy Computing and Adaptive GPU Acceleration for Distributed Edge Networks

2026-01-21

IEEE Transactions on Mobile Computing

AI Summary

This work addresses the challenges of limited computational resources, privacy leakage risks, and high communication overhead in distributed computing over edge networks. To this end, the authors propose a three-phase parallel collaborative ADMM-based privacy-preserving computation algorithm (3P-ADMM-PC²), which integrates Paillier homomorphic encryption with real-number quantization mapping to enable efficient collaborative optimization while ensuring data privacy. The method innovatively transforms high-dimensional large-integer operations into low-bit matrix computations, resolving incompatibility with GPU architectures and enabling large-scale encryption and decryption through GPU-accelerated parallelization. Experimental results demonstrate that the proposed approach achieves mean squared error comparable to non-private ADMM across various network topologies, while significantly outperforming CPU-based centralized and distributed baselines in computational speed.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Distributed computing has been widely applied in distributed edge networks for reducing the processing burden of high-dimensional data centralization, where a high-dimensional computational task is decomposed into multiple low-dimensional collaborative processing tasks or multiple edge nodes use distributed data to train a global model. However, the computing power of a single-edge node is limited, and collaborative computing will cause information leakage and excessive communication overhead. In this paper, we design a parallel collaborative distributed alternating direction method of multipliers (ADMM) and propose a three-phase parallel collaborative ADMM privacy computing (3P-ADMM-PC2) algorithm for distributed computing in edge networks, where the Paillier homomorphic encryption is utilized to protect data privacy during interactions. Especially, a quantization method is introduced, which maps the real numbers to a positive integer interval without affecting the homomorphic operations. To address the architectural mismatch between large-integer and Graphics Processing Unit (GPU) computing, we transform high-bitwidth computations into low-bitwidth matrix and vector operations. Thus the GPU can be utilized to implement parallel encryption and decryption computations with long keys. Finally, a GPU-accelerated 3P-ADMM-PC2 is proposed to optimize the collaborative computing tasks. Meanwhile, large-scale computational tasks are conducted in network topologies with varying numbers of edge nodes. Experimental results demonstrate that the proposed 3P-ADMM-PC2 has excellent mean square error performance, which is close to that of distributed ADMM without privacy-preserving. Compared to centralized ADMM and distributed ADMM implemented with Central Processing Unit (CPU) computation, the proposed scheme demonstrates a significant speedup ratio.

Problem

• privacy leakage
• communication overhead
• edge computing
• distributed computing
• limited computational resources

Innovation

• ADMM
• privacy computing
• GPU acceleration
• homomorphic encryption
• edge networks