Tootfinder

Functional Reasoning for Distributed Systems with Failures
Haobin Ni, Robbert van Renesse, Greg Morrisett
https://arxiv.org/abs/2510.12131 https://arxiv.or…

Functional Reasoning for Distributed Systems with Failures
Distributed system theory literature often argues for correctness using an informal, Hoare-like style of reasoning. While these arguments are intuitive, they have not all been foolproof, and whether they directly correspond to formal proofs is in question. We formally ground this kind of reasoning and connect it to standard formal approaches through language design and meta-analysis, which leads to a functional style of compositional formal reasoning for a class of distributed systems, includin…

Collaborative Shadows: Distributed Backdoor Attacks in LLM-Based Multi-Agent Systems
Pengyu Zhu, Lijun Li, Yaxing Lyu, Li Sun, Sen Su, Jing Shao
https://arxiv.org/abs/2510.11246

Collaborative Shadows: Distributed Backdoor Attacks in LLM-Based Multi-Agent Systems
LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks remains largely underexplored. However, existing research has focused exclusively on single-agent backdoor attacks, overlooking the novel attack surfaces introduced by agent collaboration in MAS. To bridge this gap, we present the first Distributed Backdoor Attack tailored to MAS. We decompose the backdoor into multiple distributed attack primitives that…

An Explorative Study on Distributed Computing Techniques in Training and Inference of Large Language Models
Sheikh Azizul Hakim, Saem Hasan
https://arxiv.org/abs/2510.11211 http…

An Explorative Study on Distributed Computing Techniques in Training and Inference of Large Language Models
Large language models (LLM) are advanced AI systems trained on extensive textual data, leveraging deep learning techniques to understand and generate human-like language. Today's LLMs with billions of parameters are so huge that hardly any single computing node can train, fine-tune, or infer from them. Therefore, several distributed computing techniques are being introduced in the literature to properly utilize LLMs. We have explored the application of distributed computing techniques in LLMs f…

On the Capacity of Distributed Quantum Storage
Hua Sun, Syed A. Jafar
https://arxiv.org/abs/2510.10568 https://arxiv.org/pdf/2510.10568

On the Capacity of Distributed Quantum Storage
A distributed quantum storage code maps a quantum message to N storage nodes, of arbitrary specified sizes, such that the stored message is robust to an arbitrary specified set of erasure patterns. The sizes of the storage nodes, and erasure patterns may not be homogeneous. The capacity of distributed quantum storage is the maximum feasible size of the quantum message (relative to the sizes of the storage nodes), when the scaling of the size of the message and all storage nodes by the same scal…

Distributed clustering in partially overlapping feature spaces
Alessio Maritan, Luca Schenato
https://arxiv.org/abs/2510.09799 https://arxiv.org/pdf/2510.0…

Distributed clustering in partially overlapping feature spaces
We introduce and address a novel distributed clustering problem where each participant has a private dataset containing only a subset of all available features, and some features are included in multiple datasets. This scenario occurs in many real-world applications, such as in healthcare, where different institutions have complementary data on similar patients. We propose two different algorithms suitable for solving distributed clustering problems that exhibit this type of feature space heter…

Privacy-Preserving Distributed Estimation with Limited Data Rate
Jieming Ke, Jimin Wang, Ji-Feng Zhang
https://arxiv.org/abs/2510.12549 https://arxiv.org/p…

Privacy-Preserving Distributed Estimation with Limited Data Rate
This paper focuses on the privacy-preserving distributed estimation problem with a limited data rate, where the observations are the sensitive information. Specifically, a binary-valued quantizer-based privacy-preserving distributed estimation algorithm is developed, which improves the algorithm's privacy-preserving capability and simultaneously reduces the communication costs. The algorithm's privacy-preserving capability, measured by the Fisher information matrix, is dynamically enhanced over…

dHPR: A Distributed Halpern Peaceman--Rachford Method for Non-smooth Distributed Optimization Problems
Zhangcheng Feng, Defeng Sun, Yancheng Yuan, Guojun Zhang
https://arxiv.org/abs/2511.10069 https://arxiv.org/pdf/2511.10069 https://arxiv.org/html/2511.10069
arXiv:2511.10069v1 Announce Type: new
Abstract: This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smooth objectives, which achieves a non-ergodic $O(1/k)$ iteration complexity regarding Karush--Kuhn--Tucker residual. By leveraging the symmetric Gauss--Seidel decomposition, the dHPR effectively decouples the linear operators in the objective functions and consensus constraints while maintaining parallelizability and avoiding additional large proximal terms, leading to a decentralized implementation with provably fast convergence. The superior performance of dHPR is demonstrated through comprehensive numerical experiments on distributed LASSO, group LASSO, and $L_1$-regularized logistic regression problems.
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DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems
Alessandro Cornacchia, Vaastav Anand, Muhammad Bilal, Zafar Qazi, Marco Canini
https://arxiv.org/abs/2510.11872

DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems
Agentic AI applications increasingly rely on multiple agents with distinct roles, specialized tools, and access to memory layers to solve complex tasks -- closely resembling service-oriented architectures. Yet, in the rapid evolving landscape of programming frameworks and new protocols, deploying and testing AI agents as distributed systems remains a daunting and labor-intensive task. We present DMAS-Forge, a framework designed to close this gap. DMAS-Forge decouples application logic from spec…

Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Ahmed Khaled, Satyen Kale, Arthur Douillard, Chi Jin, Rob Fergus, Manzil Zaheer
https://arxiv.org/abs/2509.10439 …

Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Modern machine learning often requires training with large batch size, distributed data, and massively parallel compute hardware (like mobile and other edge devices or distributed data centers). Communication becomes a major bottleneck in such settings but methods like Local Stochastic Gradient Descent (Local SGD) show great promise in reducing this additional communication overhead. Local SGD consists of three parts: a local optimization process, an aggregation mechanism, and an outer optimize…

GeoPipe: a Geo-distributed LLM Training Framework with enhanced Pipeline Parallelism in a Lossless RDMA-enabled Datacenter Optical Transport Network
Jun Dai, Xiaorun Wang, Kexiong Fang, Zheng Yang, Yuefeng Ji, Jiawei Zhang
https://arxiv.org/abs/2510.12064

GeoPipe: a Geo-distributed LLM Training Framework with enhanced Pipeline Parallelism in a Lossless RDMA-enabled Datacenter Optical Transport Network
The proliferation of Large Language Models (LLMs) with exponentially growing parameters is making cross-data center (DC) training an inevitable trend. However, viable strategies for extending single-DC training frameworks to multi-DC environments remain underdeveloped. We experimentally demonstrate, for the first time, a high-performance geo-distributed LLMs training framework across multiple DCs interconnected by a lossless, remote direct memory access (RDMA) enabled Datacenter Optical Transpo…

Hybrid centralized-distributed precoding in fronthaul-constrained CF-mMIMO systems
Zahra Mobini, Hien Quoc Ngo, Ardavan Rahimian, Anvar Tukmanov, David Townend, Michail Matthaiou, Simon L. Cotton
https://arxiv.org/abs/2510.12406

Hybrid centralized-distributed precoding in fronthaul-constrained CF-mMIMO systems
We investigate a fronthaul-limited cell-free massive multiple-input multiple-output (CF-mMIMO) system and propose a hybrid centralized-distributed precoding strategy that dynamically adapts to varying fronthaul and spectral efficiency (SE) requirements. The proposed approach divides users into two groups: one served by centralized precoding and the other by distributed precoding. We formulate a novel optimization problem for user grouping and power control aimed at maximizing the sum SE, subjec…

A Framework for Distributed Resource Allocation in Quantum Networks
Nitish K. Panigrahy, Leonardo Bacciottini, C. V. Hollot, Emily A. Van Milligen, Matheus Guedes de Andrade, Nageswara S. V. Rao, Gayane Vardoyan, Don Towsley
https://arxiv.org/abs/2510.09371

A Framework for Distributed Resource Allocation in Quantum Networks
We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms under the mathematical framework of Quantum Network Utility Maximization (QNUM), where utility functions quantify network performance by mapping entanglement rate and quality into a joint optimization objective. We then introduce QPrimal-Dual, a decentralized, sc…

Stabilizing the Staking Rate, Dynamically Distributed Inflation and Delay Induced Oscillations
Carlo Brunetta, Amit Chaudhary, Stefano Galatolo, Massimiliano Sala
https://arxiv.org/abs/2510.11065

Stabilizing the Staking Rate, Dynamically Distributed Inflation and Delay Induced Oscillations
Dynamically distributed inflation is a common mechanism used to guide a blockchain's staking rate towards a desired equilibrium between network security and token liquidity. However, the high sensitivity of the annual percentage yield to changes in the staking rate, coupled with the inherent feedback delays in staker responses, can induce undesirable oscillations around this equilibrium. This paper investigates this instability phenomenon. We analyze the dynamics of inflation-based reward s…

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
Simen Storesund, Kristian Valset Aars, Robin Dietrich, Nicolai Waniek
https://arxiv.org/abs/2509.10077

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
Efficient planning and sequence selection are central to intelligence, yet current approaches remain largely incompatible with biological computation. Classical graph algorithms like Dijkstra's or A* require global state and biologically implausible operations such as backtracing, while reinforcement learning methods rely on slow gradient-based policy updates that appear inconsistent with rapid behavioral adaptation observed in natural systems. We propose a biologically plausible algorithm fo…

Tigris, which is building a network of localized data storage centers that it claims can meet the compute needs of AI workloads, raised a $25M Series A (Rebecca Bellan/TechCrunch)
https://techcrunch.com/2025/10/09/this-distributed-data-stor…

This distributed data storage startup wants to take on Big Cloud | TechCrunch
Tigris has raised $25 million to expand its growing network of localized data storage centers — the storage layer for decentralized computing infrastructure.

FIDRS: A Novel Framework for Integrated Distributed Reliable Systems
Mehdi Zekriyapanah Gashti
https://arxiv.org/abs/2510.10833 https://arxiv.org/pdf/2510.…

FIDRS: A Novel Framework for Integrated Distributed Reliable Systems
In this paper we represent a new framework for integrated distributed and reliable systems. In the proposed framework we have used three parts to increase Satisfaction and Performance of this framework. At first we analyze previous frameworks related to integrated systems, then represent new proposed framework in order to improving previous framework, and we discuss its different phases. Finally we compare the results of simulation of the new framework with previous ones. In FIDRS framework, th…

SEDM: Scalable Self-Evolving Distributed Memory for Agents
Haoran Xu, Jiacong Hu, Ke Zhang, Lei Yu, Yuxin Tang, Xinyuan Song, Yiqun Duan, Lynn Ai, Bill Shi
https://arxiv.org/abs/2509.09498

SEDM: Scalable Self-Evolving Distributed Memory for Agents
Long-term multi-agent systems inevitably generate vast amounts of trajectories and historical interactions, which makes efficient memory management essential for both performance and scalability. Existing methods typically depend on vector retrieval and hierarchical storage, yet they are prone to noise accumulation, uncontrolled memory expansion, and limited generalization across domains. To address these challenges, we present SEDM, Self-Evolving Distributed Memory, a verifiable and adaptive f…

S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
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Observability and parameter estimation of a generic model for aggregated distributed energy resources
Bukunmi Gabriel Odunlami, Marcos Netto
https://arxiv.org/abs/2510.10892 htt…

Observability and parameter estimation of a generic model for aggregated distributed energy resources
We propose a novel framework for estimating the parameters of an aggregated distributed energy resources (der_a) model. First, we introduce a rigorous method to determine whether all model parameters are estimable. When they are not, our approach identifies the subset of parameters that can be estimated. The proposed framework offers new insights into the number and specific parameters that can be reliably estimated based on commonly available measurements. It also highlights the limitations of…

eye2vec: Learning Distributed Representations of Eye Movement for Program Comprehension Analysis
Haruhiko Yoshioka, Kazumasa Shimari, Hidetake Uwano, Kenichi Matsumoto
https://arxiv.org/abs/2510.11722 …

eye2vec: Learning Distributed Representations of Eye Movement for Program Comprehension Analysis
This paper presents eye2vec, an infrastructure for analyzing software developers' eye movements while reading source code. In common eye-tracking studies in program comprehension, researchers must preselect analysis targets such as control flow or syntactic elements, and then develop analysis methods to extract appropriate metrics from the fixation for source code. Here, researchers can define various levels of AOIs like words, lines, or code blocks, and the difference leads to different result…

We presented “Advancing Vulnerability Tracking and Disclosure Through an Open and Distributed Platform” at the excellent @…
#cve #vulnerability

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Mathijs van Noort, Femke Ongenae, Pieter Bonte
https://arxiv.org/abs/2509.10146 https://

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Temporal reasoning in dynamic, data-intensive environments increasingly demands expressive yet tractable logical frameworks. Traditional approaches often rely on negation to express absence or contradiction. In such contexts, Negation-as-Failure is commonly used to infer negative information from the lack of positive evidence. However, open and distributed systems such as IoT networks or the Semantic Web Negation-as-Failure semantics become unreliable due to incomplete and asynchronous data. Th…

Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation
Nirabhra Mandal, Aamodh Suresh, Carlos Nieto-Granda, Sonia Mart\'inez
https://arxiv.org/abs/2509.08242

Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation
We study a problem of multi-agent exploration with behaviorally heterogeneous robots. Each robot maps its surroundings using SLAM and identifies a set of areas of interest (AoIs) or frontiers that are the most informative to explore next. The robots assess the utility of going to a frontier using Behavioral Entropy (BE) and then determine which frontier to go to via a distributed task assignment scheme. We convert the task assignment problem into a non-cooperative game and use a distributed alg…

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Shobhit Gupta, Nikolay Sheshko, Daniel J. Dilley, Alvin Gonzales, Manish K. Singh, Zain H. Saleem
https://arxiv.org/abs/2510.08894

Gate Teleportation vs Circuit Cutting in Distributed Quantum Computing
Distributing circuits across quantum processor modules will enable the execution of circuits larger than the qubit count limitations of monolithic processors. While distributed quantum computation has primarily utilized circuit cutting, it incurs an exponential growth of sub-circuit sampling and classical post-processing overhead with an increasing number of cuts. The entanglement-based gate teleportation approach does not inherently incur exponential sampling overhead, provided that quantum in…

Quantum spread complexity as a probe of NSI, $CP$ Violation, and mass ordering in neutrino oscillations in matter
Abhishek Kumar Jha, Govind Krishna G, Anandbhai Pravinbhai Prajapati, Subhashish Banerjee
https://arxiv.org/abs/2509.09854

Quantum spread complexity as a probe of NSI, $CP$ Violation, and mass ordering in neutrino oscillations in matter
Quantum spread complexity characterizes how a quantum state evolves and becomes distributed over the Hilbert space under unitary dynamics. In this work, we employ a cost function as a quantitative measure of spread complexity. We investigate this cost function within the framework of three-flavor neutrino oscillations in vacuum and matter, incorporating the $CP$-violation phase and Non-Standard Interaction (NSI) effects, under both normal and inverted mass ordering scenarios. The cost function …

Detection of Axion Stars in Galactic Magnetic Fields
Kuldeep J. Purohit, Jitesh R. Bhatt, Subhendra Mohanty, Prashant K. Mehta
https://arxiv.org/abs/2510.11032 https://

Detection of Axion Stars in Galactic Magnetic Fields
We perform a linear mode analysis of a uniformly distributed cloud of axion-like particles (ALPs) embedded in a magnetized intergalactic medium, in order to investigate the stability of axion stars under realistic astrophysical conditions. We find that when the frequency $ω$ of transverse waves is much smaller than the collision frequency $ν_c$ of the intergalactic plasma, the conversion of ALPs into photons occurs on timescales far longer than the age of the Universe, ensuring stability of t…

Hypothesis testing for the dimension of random geometric graph
Mingao Yuan, Feng Yu
https://arxiv.org/abs/2510.11844 https://arxiv.org/pdf/2510.11844

Hypothesis testing for the dimension of random geometric graph
Random geometric graphs (RGGs) offer a powerful tool for analyzing the geometric and dependence structures in real-world networks. For example, it has been observed that RGGs are a good model for protein-protein interaction networks. In RGGs, nodes are randomly distributed over an $m$-dimensional metric space, and edges connect the nodes if and only if their distance is less than some threshold. When fitting RGGs to real-world networks, the first step is probably to input or estimate the dimens…

Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption
Jennie Couchman, Phillip Stanley-Marbell
https://arxiv.org/abs/2510.11384 https://…

Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption
A common assumption in signal processing is that underlying data numerically conforms to a Gaussian distribution. It is commonly utilized in signal processing to describe unknown additive noise in a system and is often justified by citing the central limit theorem for sums of random variables, although the central limit theorem applies only to sums of independent identically distributed random variables. However, many linear operations in signal processing take the form of weighted sums, which …

Homogenization of rate-independent elastoplastic spring network models with non-local random fields
Simone Hermann
https://arxiv.org/abs/2509.09872 https://

Homogenization of rate-independent elastoplastic spring network models with non-local random fields
We investigate the time-evolution of elastoplastic materials reinforced by randomly distributed long-range interactions. Starting from a rate-independent system on a discrete spring lattice that combines local linearized elasticity, gradient-regularized plasticity and stochastic non-local links modeling stiff fibers, we establish a discrete-to-continuum limit in the energetic formulation. We prove that as the lattice spacing tends to zero, an evolutionary solution of the discrete system converg…

Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Seokjin Go, Joongun Park, Spandan More, Hanjiang Wu, Irene Wang, Aaron Jezghani, Tushar Krishna, Divya Mahajan
https://arxiv.org/abs/2509.10371

Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
The rapid scaling of Large Language Models (LLMs) has pushed training workloads far beyond the limits of single-node analysis, demanding a deeper understanding of how these models behave across large-scale, multi-GPU systems. In this paper, we present a comprehensive characterization of LLM training across diverse real-world workloads and hardware platforms, including NVIDIA H100/H200 and AMD MI250 GPUs. We analyze dense and sparse models under various parallelism strategies -- tensor, pipeline…

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Bowen Du, Qi Li, Mingwei Ge, Xintao Li, Yongqian Liu
https://arxiv.org/abs/2510.11392

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Wind farm parameterizations are crucial for quantifying the wind-farm atmosphere interaction, where wind turbines are typically modeled as elevated momentum sinks and sources of turbulence kinetic energy (TKE). These quantities must be properly distributed to the mesoscale grid. Existing parameterizations use the single-column method. However, this method can easily lead to the errors of the spatial distribution of the sink and source, thereby impacting the accuracy of mesoscale flow simulation…

ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Nojan Sheybani, Alessandro Pegoraro, Jonathan Knauer, Phillip Rieger, Elissa Mollakuqe, Farinaz Koushanfar, Ahmad-Reza Sadeghi
https://arxiv.org/abs/2509.09787

ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the client-side. This setup enables SL to leverage server computation capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data. However, the distributed nature enables malicious clients to manipul…

Distributed Stochastic Model Predictive Control with Temporal Aggregation for the Joint Dispatch of Cascaded Hydropower and Renewables
Luca Santosuosso, Sonja Wogrin
https://arxiv.org/abs/2510.11998

Distributed Stochastic Model Predictive Control with Temporal Aggregation for the Joint Dispatch of Cascaded Hydropower and Renewables
This paper addresses the real-time energy dispatch of a hybrid system comprising cascaded hydropower plants, wind, and solar units, jointly participating in the day-ahead energy market under inflow, renewable generation, and price uncertainties. Traditional scenario-based stochastic model predictive control (MPC) faces severe computational bottlenecks due to the complexity arising from the temporal, asset, and scenario dimensions of this control problem. To address this, we propose a novel cont…

The Diameter of (Threshold) Geometric Inhomogeneous Random Graphs
Zylan Benjert, Kostas Lakis, Johannes Lengler, Raghu Raman Ravi
https://arxiv.org/abs/2510.12543 https://

The Diameter of (Threshold) Geometric Inhomogeneous Random Graphs
We prove that the diameter of threshold (zero temperature) Geometric Inhomogeneous Random Graphs (GIRG) is $Θ(\log n)$. This has strong implications for the runtime of many distributed protocols on those graphs, which often have runtimes bounded as a function of the diameter. The GIRG model exhibits many properties empirically found in real-world networks, and the runtime of various practical algorithms has empirically been found to scale in the same way for GIRG and for real-world networks,…

Polyglot Persistence in Microservices: Managing Data Diversity in Distributed Systems
Festim Halili, Anila Nuhiji, Diellza Mustafai Veliu
https://arxiv.org/abs/2509.08014 https:…

Polyglot Persistence in Microservices: Managing Data Diversity in Distributed Systems
Microservices architectures have become the foundation for developing scalable and modern software systems, but they also bring significant challenges in managing heterogeneous and distributed data. The pragmatic solution is polyglot persistence, the deliberate use of several different database technologies adapted to a given microservice requirement - is one such strategy. This paper examines polyglot persistence in microservice based systems. This paper brings together theoretical concepts wi…

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Shiwei Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Jianbin Lin, Wenliang Zhong
https://arxiv.org/abs/2509.10161

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative model training without sharing local data. Despite its advantages, FL suffers from substantial communication overhead, which can affect training efficiency. Recent efforts have mitigated this issue by quantizing model updates to reduce communication costs. However, most existing methods apply quantization only after local training, introducing quantization errors into the trained parameters a…

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
Woong Shin, Renan Souza, Daniel Rosendo, Fr\'ed\'eric Suter, Feiyi Wang, Prasanna Balaprakash, Rafael Ferreira da Silva
https://arxiv.org/abs/2509.09915

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science
Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conce…

Techniques of Artificial Intelligence Applied to Near-Infrared Spectra
Aminata Sow, Tidiane Diallo
https://arxiv.org/abs/2510.10638 https://arxiv.org/pdf/2…

Techniques of Artificial Intelligence Applied to Near-Infrared Spectra
This article explores the application of various artificial intelligence techniques to the analysis of near-infrared (NIR) spectra of paracetamol, within the spectral range of 900 nm to 1800 nm. The main objective is to evaluate the performance of several dimensionality reduction algorithms; namely, Principal Component Analysis (PCA), Kernel PCA (KPCA), Sparse Kernel PCA, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) in modeling an…

Quantum Annealing for Staff Scheduling in Educational Environments
Alessia Ciacco, Francesca Guerriero, Eneko Osaba
https://arxiv.org/abs/2510.12278 https://

Quantum Annealing for Staff Scheduling in Educational Environments
We address a novel staff allocation problem that arises in the organization of collaborators among multiple school sites and educational levels. The problem emerges from a real case study in a public school in Calabria, Italy, where staff members must be distributed across kindergartens, primary, and secondary schools under constraints of availability, competencies, and fairness. To tackle this problem, we develop an optimization model and investigate a solution approach based on quantum anneal…

Modal analysis and optimization of swimming active filaments
John Severn, Eric Lauga
https://arxiv.org/abs/2510.09627 https://arxiv.org/pdf/2510.09627

Modal analysis and optimization of swimming active filaments
Active flexible filaments form the classical continuum framework for modelling the locomotion of spermatozoa and algae driven by the periodic oscillation of flagella. This framework also applies to the locomotion of various artificial swimmers. Classical studies have quantified the relationship between internal forcing (localised or distributed internal moments or forces) and external output (filament shape and swimming speed). In this paper, we pose locomotion as a mathematical optimisation pr…

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Sehar Zehra, Hassan Jamil Syed, Ummay Faseeha
https://arxiv.org/abs/2510.10126

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network ev…

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- Large-scale semi-discrete optimal transport with distributed Voronoi diagrams
Bruno L\'evy, Nicolas Ray, Quentin M\'erigot, Hugo Leclerc

Agroseismology: unraveling the impact of farming practices on soil hydrodynamics
Qibin Shi, David R. Montgomery, Abigail L. S. Swann, Nicoleta C. Cristea, Ethan Williams, Nan You, Joe Collins, Ana Prada Barrio, Simon Jeffery, Paula A. Misiewicz, Tarje Nissen-Meyer, Marine A. Denolle
https://arxiv.org/abs/2509.09821

Agroseismology: unraveling the impact of farming practices on soil hydrodynamics
Farmed landscapes provide a natural laboratory to test how management reshapes near-surface hydrodynamics. Combining distributed acoustic sensing with physics-based hydromechanical modeling, we tracked minute-resolution, meter-scale changes across experimental fields with controlled tillage and compaction histories. We find that dynamic capillary effects, rate-dependent suction stresses during wetting and drying, govern transient stiffness and moisture redistribution in disturbed soils, produci…

Influence of Large Mean Delay on Distributed Delay Differential Equations Dynamics: Application to a Neural Mass Model
Isam Al-Darabsah, Sue Ann Campbell, Bootan Rahman
https://arxiv.org/abs/2509.05466

Influence of Large Mean Delay on Distributed Delay Differential Equations Dynamics: Application to a Neural Mass Model
Delay differential equations (DDEs) with large delays play a pivotal role in understanding stability and bifurcations in systems ranging from neural networks to laser dynamics. While prior work has extensively studied DDEs with discrete delays, the impact of distributed delays has been less explored. This paper investigates the spectrum of linear DDEs with a uniformly distributed delay kernel, with mean delay $τ_m$ and a half-width of $ρ$. When $τ_m\to\infty$, we carry out asymptotic analysi…

CoNeT-GIANT: A compressed Newton-type fully distributed optimization algorithm
Souvik Das, Subhrakanti Dey
https://arxiv.org/abs/2510.08806 https://arxiv.o…

CoNeT-GIANT: A compressed Newton-type fully distributed optimization algorithm
Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication constraints such as limited bandwidth. This article presents a communication-efficient second-order distributed optimization algorithm, termed as CoNet-GIANT, equipped with a compression module, designed to minimize the average of local strongly convex functions. CoNet-GIANT incorporates two consensus-based averaging …

QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters
Haci Ismail Aslan, Syed Muhammad Mahmudul Haque, Joel Witzke, Odej Kao
https://arxiv.org/abs/2510.09851

QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters
Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-of-Service (QoS) requirements. Standard Kubernetes schedulers do not account for user-defined objectives such as energy efficiency, cost optimization, and global performance, often leaving operators to make manual, cluster-by-cluster placement decisions. To address this need, we present QONNECT, a vendor-agnostic orch…

Active IRS Assisted Joint Uplink and Downlink Communications
Qiaoyan Peng, Qingqing Wu, Guangji Chen, Wen Chen, Shaodan Ma
https://arxiv.org/abs/2510.10045 https://

Active IRS Assisted Joint Uplink and Downlink Communications
In this paper, we investigate an intelligent reflecting surface (IRS) aided wireless communication system, where active IRSs (AIRSs) are deployed to assist communication between a base station (BS) and users of both the uplink (UL) and downlink (DL). We aim to maximize the weighted sum rate (WSR) of UL and DL communications through joint optimization of BS, AIRS beamforming, and AIRS element allocation. First, we study three deployment schemes, namely distributed AIRSs, BS-side AIRS, and user-s…

Power and limitations of distributed quantum state purification
Benchi Zhao, Yu-Ao Chen, Xuanqiang Zhao, Chengkai Zhu, Giulio Chiribella, Xin Wang
https://arxiv.org/abs/2509.08691

Power and limitations of distributed quantum state purification
Quantum state purification, which mitigates noise by exploiting multiple noisy copies of unknown states, has applications in quantum communication and computation with imperfect devices. Despite its importance, the fundamental capabilities and limitations of purification in distributed quantum systems remain largely unexplored. Here, we systematically study distributed quantum state purification under local operations and classical communication (LOCC). We prove that no nontrivial two-to-one LO…

Age of Information-Aware Cognitive Shared Access Networks with Energy Harvesting
Georgios Smpokos, Dionysis Xenakis, Marios Kountouris, Nikolaos Pappas
https://arxiv.org/abs/2510.11198

Age of Information-Aware Cognitive Shared Access Networks with Energy Harvesting
This study investigates a cognitive shared access network with energy harvesting capabilities operating under Age of Information (AoI) constraints for the primary user. Secondary transmitters are spatially distributed according to a homogeneous Poisson Point Process (PPP), while the primary user is located at a fixed position. The primary transmitter handles bursty packet arrivals, whereas secondary users operate under saturated traffic conditions. To manage interference and energy, two distinc…

A central limit theorem for unbalanced step-reinforced random walks
Zhishui Hu, Liang Dong
https://arxiv.org/abs/2510.10898 https://arxiv.org/pdf/2510.1089…

A central limit theorem for unbalanced step-reinforced random walks
In this paper, we study a class of unbalanced step-reinforced random walks that unifies the elephant random walk, the positively step-reinforced random walk, and the negatively step-reinforced random walk. By establishing a connection with bond percolation on random recursive trees, these processes can be represented as randomly weighted sums of independent and identically distributed random variables. We first derive normal and stable central limit theorems for such randomly weighted sums, and…

Distributed force element decomposition with method of fundamental solutions
Zhiteng Zhou, Yi Liu, Hongping Wang, Shizhao Wang
https://arxiv.org/abs/2509.07783 https://

Distributed force element decomposition with method of fundamental solutions
Quantifying the contribution of vortex structures to pressure stress is useful for designing flow control strategies to mitigate low or drag. The traditional force-element method focuses on the contribution of vortex structures to the resultant force. However, the contribution of vortex structures to the distributed force can not be identified. To address this problem, a distributed force element method is proposed. This method projects the Navier-Stokes equation to a divergence-free space and …

Aggregate Modeling of Air-Conditioner Loads Under Packet-based Control with Both On and Off Grid Access Requests
Mohammad Hassan, Mads R. Almassalkhi
https://arxiv.org/abs/2510.10651

Aggregate Modeling of Air-Conditioner Loads Under Packet-based Control with Both On and Off Grid Access Requests
Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibility by sending a request to the DER coordinator to turn-ON or turn-OFF. Much work has focused on modeling and analyzing aggregations of DE…

High-efficiency and long-distance quantum memory-assisted device-independent quantum secret sharing with single photon sources
Qi Zhang, Jia-Wei Ying, Shi-Pu Gu, Xing-Fu Wang, Lan Zhou, Yu-Bo Sheng
https://arxiv.org/abs/2510.12288

High-efficiency and long-distance quantum memory-assisted device-independent quantum secret sharing with single photon sources
Quantum secret sharing (QSS) plays a critical role in building the distributed quantum networks. Device-independent (DI) QSS provides the highest security level for QSS. However, the photon transmission loss and extremely low multipartite entanglement generation rate largely limit DI QSS's secure photon transmission distance and practical key generation efficiency. To address the above drawbacks, we propose the quantum memory-assisted (QMA) DI QSS protocol based on single photon sources (SPSs).…

Learning to accelerate distributed ADMM using graph neural networks
Henri Doerks, Paul H\"ausner, Daniel Hern\'andez Escobar, Jens Sj\"olund
https://arxiv.org/abs/2509.05288

Learning to accelerate distributed ADMM using graph neural networks
Distributed optimization is fundamental in large-scale machine learning and control applications. Among existing methods, the Alternating Direction Method of Multipliers (ADMM) has gained popularity due to its strong convergence guarantees and suitability for decentralized computation. However, ADMM often suffers from slow convergence and sensitivity to hyperparameter choices. In this work, we show that distributed ADMM iterations can be naturally represented within the message-passing framewor…

Multi-Robot Distributed Optimization for Exploration and Mapping of Unknown Environments using Bioinspired Tactile-Sensor
Roman Ibrahimov, Jannik Matthias Heinen
https://arxiv.org/abs/2510.06085

Multi-Robot Distributed Optimization for Exploration and Mapping of Unknown Environments using Bioinspired Tactile-Sensor
This project proposes a bioinspired multi-robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each robot explores its environment and creates a map, which is afterwards put together to form a global 2D map of the environment. Inspired by wall-following behaviors, each robot autonomously explores its neighborhood based on a tactile sensor, similar to the antenna of a cockroach, mounted on the surface of the robot. Instead of avoiding obstacl…

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
Benjamin Brock, Renato Golin
https://arxiv.org/abs/2510.08874 https://

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
Many important applications across science, data analytics, and AI workloads depend on distributed matrix multiplication. Prior work has developed a large array of algorithms suitable for different problem sizes and partitionings including 1D, 2D, 1.5D, and 2.5D algorithms. A limitation of current work is that existing algorithms are limited to a subset of partitionings. Multiple algorithm implementations are required to support the full space of possible partitionings. If no algorithm implemen…

GCVE-BCP-05 - GCVE Vulnerability Format (Updated CVE Record Format) has been published as DRAFT and ready for public review.
The standard is similar to the @… record format with some extensions (via the X_ prefixes) for GCVE format and the reference implementation vulnerability-lookup. This allows some flexibility and innovation in GNA - GCVE space w…

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki, Kerstin Eder
https://arxiv.org/abs/2510.09842

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems un…

Comparative Performance Analysis of Modern NoSQL Data Technologies: Redis, Aerospike, and Dragonfly
Deep Bodra, Sushil Khairnar
https://arxiv.org/abs/2510.08863 https://

Comparative Performance Analysis of Modern NoSQL Data Technologies: Redis, Aerospike, and Dragonfly
The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across three distinct workload patterns (read-heavy, write-heavy), and balanced while systematically varying client concurrency from 1 to 32 clients…

Optimizing Inter-chip Coupler Link Placement for Modular and Chiplet Quantum Systems
Zefan Du, Pedro Chumpitaz Flores, Wenqi Wei, Juntao Chen, Kaixun Hua, Ying Mao
https://arxiv.org/abs/2509.10409

Optimizing Inter-chip Coupler Link Placement for Modular and Chiplet Quantum Systems
Quantum computing offers unparalleled computational capabilities but faces significant challenges, including limited qubit counts, diverse hardware topologies, and dynamic noise and error rates, which hinder scalability and reliability. Distributed quantum computing, particularly chip-to-chip connections, has emerged as a solution by interconnecting multiple processors to collaboratively execute large circuits. While hardware advancements, such as IBM's Quantum Flamingo, focus on improving inte…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- LLMBridge: Reducing Costs in a Prompt-Centric Internet
Noah Martin, Abdullah Bin Faisal, Hiba Eltigani, Rukhshan Haroon, Swaminathan Lamelas, Fahad Dogar

Efficient LLM Inference over Heterogeneous Edge Networks with Speculative Decoding
Bingjie Zhu, Zhixiong Chen, Liqiang Zhao, Hyundong Shin, Arumugam Nallanathan
https://arxiv.org/abs/2510.11331

Efficient LLM Inference over Heterogeneous Edge Networks with Speculative Decoding
Large language model (LLM) inference at the network edge is a promising serving paradigm that leverages distributed edge resources to run inference near users and enhance privacy. Existing edge-based LLM inference systems typically adopt autoregressive decoding (AD), which only generates one token per forward pass. This iterative process, compounded by the limited computational resources of edge nodes, results in high serving latency and constrains the system's ability to support multiple users…

Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
Joachim Diederich
https://arxiv.org/abs/2510.09338 https://arxiv.org/pdf/2510.09338

Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining. This is achieved through group sparsity penalties on attention mechanisms, …

RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
Arefin Niam, Tevfik Kosar, M S Q Zulkar Nine
https://arxiv.org/abs/2509.05207

RapidGNN: Energy and Communication-Efficient Distributed Training on Large-Scale Graph Neural Networks
Graph Neural Networks (GNNs) have become popular across a diverse set of tasks in exploring structural relationships between entities. However, due to the highly connected structure of the datasets, distributed training of GNNs on large-scale graphs poses significant challenges. Traditional sampling-based approaches mitigate the computational loads, yet the communication overhead remains a challenge. This paper presents RapidGNN, a distributed GNN training framework with deterministic sampling-…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Rationally Analyzing Shelby: Proving Incentive Compatibility in a Decentralized Storage Network
Michael Crystal, Guy Goren, Scott Duke Kominers

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Zihao Mao, Yunheng Wang, Yunting Ji, Yi Yang, Wenjie Song
https://arxiv.org/abs/2510.09188

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Multi-robot navigation in unknown, structurally constrained, and GPS-denied environments presents a fundamental trade-off between global strategic foresight and local tactical agility, particularly under limited communication. Centralized methods achieve global optimality but suffer from high communication overhead, while distributed methods are efficient but lack the broader awareness to avoid deadlocks and topological traps. To address this, we propose a fully decentralized, hierarchical rela…

Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communications
Tianyu Huo, Jian Xiong, Yiyan Wu, Songjie Yang, Bo Liu, Wenjun Zhang
https://arxiv.org/abs/2509.06967

Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communications
Extremely large antenna array (ELAA) is key to enhancing spectral efficiency in 6G networks. Leveraging the distributed nature of multi-unmanned aerial vehicle (UAV) systems enables the formation of distributed ELAA, which often operate in the near-field region with spatial sparsity, rendering the conventional far-field plane wave assumption invalid. This paper investigates channel estimation for distributed near-field multi-UAV communication systems. We first derive closed-form signal-to-noise…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
Yebo Wu, Jingguang Li, Chunlin Tian, Kahou Tam, Li Li, Chengzhong Xu

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Jack Jackman, David Ryan, Arun Narayanan, Pedro Nardelli, Indrakshi Dey
https://arxiv.org/abs/2510.11286

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fo…

Distributed-HISQ: A Distributed Quantum Control Architecture
Yilun Zhao, Kangding Zhao, Peng Zhou, Dingdong Liu, Tingyu Luo, Yuzhen Zheng, Peng Luo, Shun Hu, Jin Lin, Cheng Guo, Yinhe Han, Ying Wang, Mingtang Deng, Junjie Wu, X. Fu
https://arxiv.org/abs/2509.04798

Distributed-HISQ: A Distributed Quantum Control Architecture
The design of a scalable Quantum Control Architecture (QCA) faces two primary challenges. First, the continuous growth in qubit counts has rendered distributed QCA inevitable, yet the nondeterministic latencies inherent in feedback loops demand cycleaccurate synchronization across multiple controllers. Existing synchronization strategies -- whether lock-step or demand-driven -- introduce significant performance penalties. Second, existing quantum instruction set architectures are polarized, bei…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- A Semantic Model for Audit of Cloud Engines based on ISO/IEC TR 3445:2022
Morteza Sargolzaei Javan

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
Nico Bistolfi, Andreea Georgescu, Dave Hodson
https://arxiv.org/abs/2510.09494 …

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
As cloud infrastructure evolves to support dynamic and distributed workflows, accelerated now by AI-driven processes, the outdated model of standing permissions has become a critical vulnerability. Based on the Cloud Security Alliance (CSA) Top Threats to Cloud Computing Deep Dive 2025 Report, our analysis details how standing permissions cause catastrophic cloud breaches. While current security tools are addressing network and API security, the challenge of securing granular data access remain…

Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)
Zhongyuan Zhao, Gunjan Verma, Ananthram Swami, Santiago Segarra
https://arxiv.org/abs/2509.05447

Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)
In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congestion, energy consumption, and radio footprint expansion. To mitigate these challenges, we propose a distributed link sparsification scheme employing graph neural networks (GNNs) to reduce scheduling overhead for delay-tolerant traffic while maintaining network capacity. A GNN module is trained to adjust contention thres…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- DBOS Network Sensing: A Web Services Approach to Collaborative Awareness
Sophia Lockton, et al.

Data-driven optimization of sparse sensor placement in thermal hydraulic experiments
Xicheng Wang, Yun. Feng, Dmitry Grishchenko, Pavel Kudinov, Ruifeng Tian, Sichao Tan
https://arxiv.org/abs/2509.10055

Data-driven optimization of sparse sensor placement in thermal hydraulic experiments
Thermal-Hydraulic (TH) experiments provide valuable insight into the physics of heat and mass transfer and qualified data for code development, calibration and validation. However, measurements are typically collected from sparsely distributed sensors, offering limited coverage over the domain of interest and phenomena of interest. Determination of the spatial configuration of these sensors is crucial and challenging during the pre-test design stage. This paper develops a data-driven framework …

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin, Ivan Kireev, Andrey Savchenko
https://arxiv.org/abs/2510.09566

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their application to specific tasks. Solving such problems is crucial because the need for scalable and resource-efficient models is increasing. To address these challenges, we have developed a new automated machine learning (AutoML) framework, Parameter Efficient Training with Robust Automation (PETRA). It applies evolutionary op…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Round-Optimal Approximate Agreement on Trees
Marc Fuchs, Diana Ghinea, Zahra Parsaeian

Fair Risk Optimization of Distributed Systems
Aray Almen, Darinka Dentcheva
https://arxiv.org/abs/2509.05737 https://arxiv.org/pdf/2509.05737

Fair Risk Optimization of Distributed Systems
The paper provides a framework for the assessment and optimization of the total risk of complex distributed systems. The framework takes into account the risk of each agent, which may arise from heterogeneous sources, as well as the risk associated with the efficient operation of the system as a whole. The challenges posed by this task are associated with the lack of additivity of risk, the need to evaluate the risk of every agent (unit) using confidential or proprietary information, and the …

Partitioning and Self-organization of Distributed Generation in Large Distribution Networks
Badr Al Faiya, Stephen McArthur, Ivana Kockar
https://arxiv.org/abs/2509.07918 https:…

Partitioning and Self-organization of Distributed Generation in Large Distribution Networks
Distribution networks will experience more installations of distributed generation (DG) that is unpredictable and stochastic in nature. Greater distributed control and intelligence will allow challenges such as voltage control to be handled effectively. The partitioning of power networks into smaller clusters provides a method to split the control problem into manageable sub-problems. This paper presents a community detection-based partitioning technique for distribution networks considering lo…

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Yuanjun Dai, Keqiang He, An Wang
https://arxiv.org/abs/2510.08522 https://

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Existing batch size selection approaches in dis- tributed machine learning rely on static allocation or simplistic heuristics that fail to adapt to heterogeneous, dynamic computing environments. We present DYNAMIX, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO). Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource uti…

Distributed Unknown Input Observer Design with Relaxed Conditions: Theory and Application to Vehicle Platooning
Ruixuan Zhao, Guitao Yang, Thomas Parisini, Boli Chen
https://arxiv.org/abs/2509.08783

Distributed Unknown Input Observer Design with Relaxed Conditions: Theory and Application to Vehicle Platooning
Designing observers for linear systems with both known and unknown inputs is an important problem in several research contexts, for example, fault diagnosis and fault-tolerant control, and cyber-secure control systems, and presents significant challenges in distributed state estimation due to the limited sensing capabilities of individual nodes. Existing methods typically impose an individual input-to-output rank condition on each estimator node, which severely restricts applicability in practi…

[2025-10-15 Wed (UTC), 8 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
toXiv_bot_toot

Quantum Channel Masking
Anna Honeycutt, Hailey Murray, Eric Chitambar
https://arxiv.org/abs/2510.09456 https://arxiv.org/pdf/2510.09456

Quantum Channel Masking
Quantum masking is a special type of secret sharing in which some information gets reversibly distributed into a multipartite system, leaving the original information inaccessible to each subsystem. This paper proposes a dynamical extension of quantum masking to the level of quantum channels. In channel masking, the identity of a channel becomes locally hidden but still globally accessible after its output is sent through a bipartite broadcasting channel. We first characterize all families of d…

[2025-10-14 Tue (UTC), 14 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
toXiv_bot_toot

On the Perturbed Projection-Based Distributed Gradient-Descent Algorithm: A Fully-Distributed Adaptive Redesign
Tarek Bazizi, Mohamed Maghenem, Paolo Frasca, Antonio Lor\`ia, Elena Panteley
https://arxiv.org/abs/2509.03443

On the Perturbed Projection-Based Distributed Gradient-Descent Algorithm: A Fully-Distributed Adaptive Redesign
In this work, we revisit a classical distributed gradient-descent algorithm, introducing an interesting class of perturbed multi-agent systems. The state of each subsystem represents a local estimate of a solution to the global optimization problem. Thereby, the network is required to minimize local cost functions, while gathering the local estimates around a common value. Such a complex task suggests the interplay of consensus-based dynamics with gradient-descent dynamics. The latter descent d…

[2025-09-15 Mon (UTC), 3 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
toXiv_bot_toot

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu
https://arxiv.org/abs/2509.07342

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a tempor…

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
Ruixuan Zhao, Guitao Yang, Peng Li, Boli Chen
https://arxiv.org/abs/2509.08914 https://

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
State estimation for linear time-invariant systems with unknown inputs is a fundamental problem in various research domains. In this article, we establish conditions for the design of unknown input observers (UIOs) from a geometric approach perspective. Specifically, we derive a necessary and sufficient geometric condition for the existence of a centralized UIO. Compared to existing results, our condition offers a more general design framework, allowing designers the flexibility to estimate par…

Nonlocal Games Through Communication Complexity and Quantum Cryptography
Pierre Botteron
https://arxiv.org/abs/2510.09457 https://arxiv.org/pdf/2510.09457

Nonlocal Games Through Communication Complexity and Quantum Cryptography
This thesis explores foundational aspects of quantum information theory and quantum cryptography. First, we investigate quantum correlations in interactive settings, including the CHSH and graph isomorphism games. We aim to distinguish quantum correlations from non-signaling correlations by leveraging the principle of communication complexity. To this end, we employ techniques such as distributed computation, majority-function-based distillation protocols, the algebraic and geometric properti…

Resilient Global Practical Fixed-Time Cooperative Output Regulation of Uncertain Nonlinear Multi-Agent Systems Subject to Denial-of-Service Attacks
Wenji Cao, Lu Liu, Zehua Ye, Dan Zhang, Gang Feng
https://arxiv.org/abs/2509.08324

Resilient Global Practical Fixed-Time Cooperative Output Regulation of Uncertain Nonlinear Multi-Agent Systems Subject to Denial-of-Service Attacks
This paper investigates the problem of resilient global practical fixed-time cooperative output regulation of uncertain nonlinear multi-agent systems subject to denial-of-service attacks. A novel distributed resilient adaptive fixed-time control strategy is proposed, which consists of a novel distributed resilient fixed-time observer with a chain of nonlinear filters and a novel distributed resilient adaptive fixed-time controller. It is shown that the problem of resilient global practical fixe…

THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling
Said Muhammad, Lahlou Laaziz, Nadjia Kara, Phat Tan Nguyen, Timothy Murphy
https://arxiv.org/abs/2510.09847

THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling
The dynamic adaptation of resource levels enables the system to enhance energy efficiency while maintaining the necessary computational resources, particularly in scenarios where workloads fluctuate significantly over time. The proposed approach can play a crucial role in heterogeneous systems where workload characteristics are not uniformly distributed, such as non-pinning tasks. The deployed THEAS algorithm in this research work ensures a balance between performance and power consumption, mak…

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
Quentin Renau, Amjad Ullah, Emma Hart
https://arxiv.org/abs/2510.08228 https://arxiv.…

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distribu…

Distributed Platoon Control Under Quantization: Stability Analysis and Privacy Preservation
Kaixiang Zhang, Zhaojian Li, Wei Lin
https://arxiv.org/abs/2510.05959 https://…

Distributed Platoon Control Under Quantization: Stability Analysis and Privacy Preservation
Distributed control of connected and automated vehicles has attracted considerable interest for its potential to improve traffic efficiency and safety. However, such control schemes require sharing privacy-sensitive vehicle data, which introduces risks of information leakage and potential malicious activities. This paper investigates the stability and privacy-preserving properties of distributed platoon control under two types of quantizers: deterministic and probabilistic. For deterministic qu…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Are Voters Willing to Collectively Secure Elections? Unraveling a Practical Blockchain Voting System
Li, Sonmezler, Shirazi, Shaji, Mroczkowski, Lardner, Camus, Pournaras

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- FAST: An Efficient Scheduler for All-to-All GPU Communication
Lei, Lee, Zhao, Kurniawan, Kim, Jeong, Kim, Choi, Yu, Krishnamurthy, Sherry, Nurvitadhi

[2025-10-13 Mon (UTC), 3 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
toXiv_bot_toot

A Simple and Robust Protocol for Distributed Counting
Edith Cohen, Moshe Shechner, Uri Stemmer
https://arxiv.org/abs/2509.05870 https://arxiv.org/pdf/2509.…

A Simple and Robust Protocol for Distributed Counting
We revisit the distributed counting problem, where a server must continuously approximate the total number of events occurring across $k$ sites while minimizing communication. The communication complexity of this problem is known to be $Θ(\frac{k}ε\log N)$ for deterministic protocols. Huang, Yi, and Zhang (2012) showed that randomization can reduce this to $Θ(\frac{\sqrt{k}}ε\log N)$, but their analysis is restricted to the {\em oblivious setting}, where the stream of events is independent …

The R(1)W(1) Communication Model for Self-Stabilizing Distributed Algorithms
Hirotsugu Kakugawa, Sayaka Kamei, Masahiro Shibata, Fukuhito Ooshita
https://arxiv.org/abs/2510.04644

The R(1)W(1) Communication Model for Self-Stabilizing Distributed Algorithms
Self-stabilization is a versatile methodology in the design of fault-tolerant distributed algorithms for transient faults. A self-stabilizing system automatically recovers from any kind and any finite number of transient faults. This property is specifically useful in modern distributed systems with a large number of components. In this paper, we propose a new communication and execution model named the R(1)W(1) model in which each process can read and write its own and neighbors' local variabl…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- SwitchFS: Asynchronous Metadata Updates for Distributed Filesystems with In-Network Coordination
Jingwei Xu, Mingkai Dong, Qiulin Tian, Ziyi Tian, Tong Xin, Haibo Chen

iDDS: Intelligent Distributed Dispatch and Scheduling for Workflow Orchestration
Wen Guan, Tadashi Maeno, Aleksandr Alekseev, Fernando Harald Barreiro Megino, Kaushik De, Edward Karavakis, Alexei Klimentov, Tatiana Korchuganova, FaHui Lin, Paul Nilsson, Torre Wenaus, Zhaoyu Yang, Xin Zhao
https://arxiv.org/abs/2510.02930

iDDS: Intelligent Distributed Dispatch and Scheduling for Workflow Orchestration
The intelligent Distributed Dispatch and Scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the Large Hadron Collider, iDDS has evolved into an experiment-agnostic pla…