61 citations · 245 across the 30 of their papers we have counts for
5 papers · 1 filter
Privacy-Preserving Distributed Optimisation using Stochastic PDMM
Sebastian O. Jordan, Qiongxiu Li, Richard Heusdens
Privacy-preserving distributed processing has received considerable attention recently. The main purpose of these algorithms is to solve certain signal processing tasks over a netw…
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-…
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus
Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaa-Zwakenberg +1
Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches…
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…
Sensor Selection using the Two-Target Cramér-Rao Bound for Angle of Arrival Estimation
Costas A. Kokke, Mario Coutiño, Laura Anitori +2
Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Alth…