2021/05/30 by Qiongxiu Li, Li, Qiongxiu, Richard Heusdens +3 · 2 citations
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Parallel #Signal Processing (eess.SP) #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC) #cs.DC #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.14416
arxiv created 2021/05/30 · openalex publication_date 2021/05/30 · arxiv updated 2021/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.