61 citations · 245 across the 30 of their papers we have counts for
5 papers · 1 filter
Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss
Wenrui Yu, Richard Heusdens, Jun Pang +1
In distributed networks, calculating the maximum element is a fundamental task in data analysis, known as the distributed maximum consensus problem. However, the sensitive nature o…
Topology-Dependent Privacy Bound For Decentralized Federated Learning
Qiongxiu Li, Wenrui Yu, Changlong Ji +1
Decentralized Federated Learning (FL) has attracted significant attention due to its enhanced robustness and scalability compared to its centralized counterpart. It pivots on peer-…
Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM
Richard Heusdens, Guoqiang Zhang
In this paper, we consider the problem of distributed optimisation of a separable convex cost function over a graph, where every edge and node in the graph could carry both linear…
Communication efficient privacy-preserving distributed optimization using adaptive differential quantization
Qiongxiu Li, Richard Heusdens, Mads Græsbøll Christensen
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 priva…
Distributed Optimization Using the Primal-Dual Method of Multipliers
G. Zhang, R. Heusdens
In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over…