Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

• Published in: Signal processing Vol. 194; p. 108456
• Main Authors: Li, Qiongxiu, Heusdens, Richard, Christensen, Mads Græsbøll
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2022
• Subjects: ADMM; Communication cost; Distributed optimization; Information-theoretic; PDMM; Privacy; Quantization; Privacy; PDMM; Distributed optimization; Communication cost; Quantization; ADMM; Information-theoretic
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2022.108456

•Privacy, accuracy and communication efficiency are major concerns in distributed computing systems and they are achieved simultaneously with the proposed approach.•Information theoretical privacy-preserving methods often incur a privacy and communication bandwidth trade-off.•The connection of quantization and privacy-preservation is established with adaptive differential quantization.•Accuracy is not compromised by considering communication efficiency and privacy.•The result is of high practical values. Privacy issues and communication cost are both major concerns in distributed optimization in networks. There is often a trade-off between them because the encryption methods used for privacy-preservation often require expensive communication overhead. To address these issues, 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). We consider two widely used adversary models, passive and eavesdropping, and investigate the properties of the proposed approach using different applications and demonstrate its superior performance compared to existing privacy-preserving approaches in terms of both accuracy and communication cost.