Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

TL;DR

This paper introduces an adaptive differential quantization method for distributed optimization that reduces communication costs while preserving privacy, applicable to various algorithms and adversary models.

Contribution

It proposes a novel quantization scheme that balances privacy and communication efficiency in distributed optimization, adaptable to multiple algorithms and threat models.

Findings

Achieves low communication cost with maintained optimization accuracy.

Provides privacy protection against passive and eavesdropping adversaries.

Demonstrates superior performance in multiple applications.

Abstract

Privacy issues and communication cost are both major concerns in distributed optimization. There is often a trade-off between them because the encryption methods required for privacy-preservation often incur expensive communication bandwidth. To address this issue, we, in this paper, propose a quantization-based approach to achieve both communication efficient and privacy-preserving solutions in the context of distributed optimization. By deploying an adaptive differential quantization scheme, we allow each node in the network to achieve its optimum solution with a low communication cost while keeping its private data unrevealed. Additionally, the proposed approach is general and can be applied in various distributed optimization methods, such as the primal-dual method of multipliers (PDMM) and the alternating direction method of multipliers (ADMM). Moveover, we consider two widely used adversary models: passive and eavesdropping. Finally, we investigate the properties of the proposed approach using different applications and demonstrate its superior performance in terms of several parameters including accuracy, privacy, and communication cost.