On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs

• URL: http://arxiv.org/abs/2508.11863v1
• Date: Sat, 16 Aug 2025 01:29:16 GMT
• Title: On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs
• Authors: Mansi Sood, Eray Can Elumar, Osman Yagan,
• Abstract summary: Sparsity ensures communication overhead remains low, while reliable connectivity is tied to reliable communication and inference on decentralized data reservoirs and computational resources.<n>A class of network models called random K-out graphs appear widely as a to balance connectivity and sparsity, especially in settings with limited trust.<n>We present theorems to inform the choice of network parameters that guarantee reliable connectivity in regimes where nodes can be finite or unreliable.
• Score: 8.957579200590985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In several applications in distributed systems, an important design criterion is ensuring that the network is sparse, i.e., does not contain too many edges, while achieving reliable connectivity. Sparsity ensures communication overhead remains low, while reliable connectivity is tied to reliable communication and inference on decentralized data reservoirs and computational resources. A class of network models called random K-out graphs appear widely as a heuristic to balance connectivity and sparsity, especially in settings with limited trust, e.g., privacy-preserving aggregation of networked data in which networks are deployed. However, several questions remain regarding how to choose network parameters in response to different operational requirements, including the need to go beyond asymptotic results and the ability to model the stochastic and adversarial environments. To address this gap, we present theorems to inform the choice of network parameters that guarantee reliable connectivity in regimes where nodes can be finite or unreliable. We first derive upper and lower bounds for probability of connectivity in random K-out graphs when the number of nodes is finite. Next, we analyze the property of r-robustness, a stronger notion than connectivity that enables resilient consensus in the presence of malicious nodes. Finally, motivated by aggregation mechanisms based on pairwise masking, we model and analyze the impact of a subset of adversarial nodes, modeled as deletions, on connectivity and giant component size - metrics that are closely tied to privacy guarantees. Together, our results pave the way for end-to-end performance guarantees for a suite of algorithms for reliable inference on networks.

Related papers

• A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
  Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
  arXiv  Detail & Related papers  (2026-02-23T16:12:02Z)
• From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network [3.632251954989679]
  HABNN is a novel approach that uses a Gaussian-inverse-Wishart distribution as a hyperprior of the network's weights.<n>Results indicate that HABNN not only matches but often outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2025-12-15T09:08:42Z)
• A Dirichlet stochastic block model for composition-weighted networks [0.0]
  We propose a block model for composition-weighted networks based on direct modelling of compositional weight vectors.<n>Inference is implemented via an extension of the classification expectation-maximisation algorithm.<n>The model is validated using simulation studies, and showcased on network data from the Erasmus exchange program and a bike sharing network for the city of London.
  arXiv  Detail & Related papers  (2024-08-01T15:41:07Z)
• Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks [59.43433767253956]
  We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network. In a semi-decentralized setup, nodes can collaborate with their neighbors to compute a local consensus, which they relay to a central server. We study the tradeoff between collaborative relaying and privacy leakage due to the data sharing among nodes.
  arXiv  Detail & Related papers  (2024-06-06T06:12:15Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy [86.61829236732744]
  This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity. The goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes.
  arXiv  Detail & Related papers  (2023-02-28T19:17:03Z)
• Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks [18.4005860362025]
  Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs) We propose a novel mechanism to protect nodes' features and edges against PIAs under differential privacy (DP) guarantees. We derive significantly better randomization probabilities and tighter error bounds at both levels of nodes' features and edges.
  arXiv  Detail & Related papers  (2022-11-10T18:52:46Z)
• Adversarial Robustness of Probabilistic Network Embedding for Link Prediction [24.335469995826244]
  We study adversarial robustness of Conditional Network Embedding (CNE) for link prediction. We measure the sensitivity of the link predictions of the model to small adversarial perturbations of the network. Our approach allows one to identify the links and non-links in the network that are most vulnerable to such perturbations.
  arXiv  Detail & Related papers  (2021-07-05T11:07:35Z)
• Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks [82.61546580149427]
  Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. This paper provides approximation and statistical guarantees of GANs for the estimation of data distributions with densities in a H"older space.
  arXiv  Detail & Related papers  (2020-02-10T16:47:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.