Tootfinder

Multi-Agent Guided Policy Optimization
Yueheng Li, Guangming Xie, Zongqing Lu
https://arxiv.org/abs/2507.18059 https://arxiv.org/pdf/2507.18059

Multi-Agent Guided Policy Optimization
Due to practical constraints such as partial observability and limited communication, Centralized Training with Decentralized Execution (CTDE) has become the dominant paradigm in cooperative Multi-Agent Reinforcement Learning (MARL). However, existing CTDE methods often underutilize centralized training or lack theoretical guarantees. We propose Multi-Agent Guided Policy Optimization (MAGPO), a novel framework that better leverages centralized training by integrating centralized guidance with d…

A Distributed Approach for Agile Supply Chain Decision-Making Based on Network Attributes
Mingjie Bi, Dawn M. Tilbury, Siqian Shen, Kira Barton
https://arxiv.org/abs/2507.19038 …

A Distributed Approach for Agile Supply Chain Decision-Making Based on Network Attributes
In recent years, the frequent occurrence of disruptions has had a negative impact on global supply chains. To stay competitive, enterprises strive to remain agile through the implementation of efficient and effective decision-making strategies in reaction to disruptions. A significant effort has been made to develop these agile disruption mitigation approaches, leveraging both centralized and distributed decision-making strategies. Though trade-offs of centralized and distributed approaches hav…

GPUnion: Autonomous GPU Sharing on Campus
Yufang Li (The Hong Kong University of Science,Technology), Yuanbo Zhang (Sun Yat-sen University), Hanlong Liao (National University of Defense Technology), Guoming Tang (The Hong Kong University of Science,Technology), Deke Guo (Sun Yat-sen University)
https://arxiv.org/abs/2507.18928

GPUnion: Autonomous GPU Sharing on Campus
A pronounced imbalance in GPU resources exists on campus, where some laboratories own underutilized servers while others lack the compute needed for AI research. GPU sharing can alleviate this disparity, while existing platforms typically rely on centralized oversight and persistent allocation models, conflicting with the voluntary and autonomous nature of academic resource ownership. We present GPUnion, a campus-scale GPU sharing platform enabling voluntary participation while preserving full …

From HOSTS.TXT to Modern Internet Infrastructure
🌐 #hoststxt

From HOSTS.TXT to Modern Internet Infrastructure | AXON Shield
The development of DNS demonstrates an impressive journey from its initial basic form into a modern distributed system which provides high resilience. The internet initially used a basic centralized text file named HOSTS.TXT for its operations. The rapid internet expansion made the initial text file system unworkable so developers created a new solution which could scale dynamically. The system evolves because organizations need better scalability and absolute reliability along with robust secu…

When the FCC approved Skydance's takeover last night,
I emailed Scheiring for his take because he described, in an essay for Politico Mag last fall, how the authoritarian Hungarian president Orbšn
"consolidated media control through centralized propaganda, market pressure and loyal billionaires."
 
Consider some of this week's media stories:
Trump is saying he has a secret side deal with incoming Paramount owner David Ellison.
Not conte…

Harnessing Diet and Gene Expression Insights through a Centralized Nutrigenomics Database to Improve Public Health
Fahmida Hai, Shriya Samudrala, Ijeoma Ezengwa, Rubayat Khan, Saif Nirzhor, Don Roosan
https://arxiv.org/abs/2506.19093

Harnessing Diet and Gene Expression Insights through a Centralized Nutrigenomics Database to Improve Public Health
Nutrigenomics is an emerging field that explores the intricate interaction between genes and diet. This study aimed to develop a comprehensive database to help clinicians and patients understand the connections between genetic disorders, associated genes, and tailored nutritional recommendations.

Practical and Accurate Local Edge Differentially Private Graph Algorithms
Pranay Mundra, Charalampos Papamanthou, Julian Shun, Quanquan C. Liu
https://arxiv.org/abs/2506.20828

Practical and Accurate Local Edge Differentially Private Graph Algorithms
The rise of massive networks across diverse domains necessitates sophisticated graph analytics, often involving sensitive data and raising privacy concerns. This paper addresses these challenges using local differential privacy (LDP), which enforces privacy at the individual level, where no third-party entity is trusted, unlike centralized models that assume a trusted curator. We introduce novel LDP algorithms for two fundamental graph statistics: k-core decomposition and triangle counting. Our…

The US is finally building its National Database of Ruin.
https://theintercept.com/2025/05/22/intel-agencies-buying-data-portal-privacy/

U.S. Spy Agencies Are Getting a One-Stop Shop to Buy Your Most Sensitive Personal Data
The government wants to build a centralized platform where spy agencies can more easily buy private info about millions of people.

DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization
Milad Hasanzadeh, Amin Kargarian
https://arxiv.org/abs/2506.20819

DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization
\textit{DPLib} is an open-source MATLAB-based benchmark library created to support research and development in distributed and decentralized power system analysis and optimization. Distributed and decentralized methods offer scalability, privacy preservation, and resilience to single points of failure, making them increasingly important for modern power systems. However, unlike centralized tools such as MATPOWER, no general-purpose, reproducible data library package currently exists for distrib…

Heterogeneous Risk Management Using a Multi-Agent Framework for Supply Chain Disruption Response
Mingjie Bi, Juan-Alberto Estrada-Garcia, Dawn M. Tilbury, Siqian Shen, Kira Barton
https://arxiv.org/abs/2507.19049

Heterogeneous Risk Management Using a Multi-Agent Framework for Supply Chain Disruption Response
In the highly complex and stochastic global, supply chain environments, local enterprise agents seek distributed and dynamic strategies for agile responses to disruptions. Existing literature explores both centralized and distributed approaches, while most work neglects temporal dynamics and the heterogeneity of the risk management of individual agents. To address this gap, this letter presents a heterogeneous risk management mechanism to incorporate uncertainties and risk attitudes into agent …

FastReChain: Highly Responsive and Low-Overhead Centralized Route Scheduling in Clos Datacenter Networks
Zihan Zhu, Dongchao Wu, Zhanbang Zhang, Jian Yang
https://arxiv.org/abs/2507.12265

FastReChain: Highly Responsive and Low-Overhead Centralized Route Scheduling in Clos Datacenter Networks
Ever since Clos topologies were used in datacenter networks (DCNs), a practical centralized scheduling algorithm that supports dynamic scheduling has been absent. The introduction of optical switches in DCNs as a future-proof solution exacerbates this problem due to several properties of optical switches, such as the fact that they are generally bufferless and therefore rely on centralized scheduling, and that they have long switching times and therefore require the number of rearrangements to …

Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
Lucas Mauser, Eva Zimmermann, Pavel Nedv\v{e}dick\'y, Tobias Eisenreich, Moritz W\"aschle, Stefan Wagner
https://arxiv.org/abs/2506.05822

Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
Centralized electrical/electronic architectures and High-Performance Computers (HPCs) are redefining automotive software development, challenging traditional microcontroller-based approaches. Ensuring real-time, safety, and scalability in software-defined vehicles necessitates reevaluating how mixed-criticality software is integrated into centralized architectures. While existing research on automotive SoftWare Architectures (SWAs) is relevant to the industry, it often lacks validation through …

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Joon Tai Kim, Tianle Chen, Ziyu Dong, Nishanth Kunchala, Alexander Guller, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
https://arxiv.org/abs/2507.06321

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire…

@… what if you built a service that centralized all of these sources so that you didn’t need to make a decision about which one to use

Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
Harsha Sammangi (Dakota State University), Aditya Jagatha (College of Business,Information Systems, Dakota State University), Giridhar Reddy Bojja (College of Business, Michigan Technological University), Jun Liu (College of Business,I.S, Dakota State University)

Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximiz…

Gemini and Glassnode research: centralized treasuries, including governments, ETFs, and public companies, now control 30.9% of bitcoin's circulating supply (Martin Young/Cointelegraph)
https://cointelegraph.com/news/bitcoin-treasuries…

General Linear-Quadratic Mean Field Stochastic Differential Game with Common Noise: a Direct Method
Yu Si, Jingtao Shi
https://arxiv.org/abs/2506.16779 htt…

General Linear-Quadratic Mean Field Stochastic Differential Game with Common Noise: a Direct Method
This paper investigates a class of general linear-quadratic mean field games with common noise, where the diffusion terms of the system contain the state variables, control variables, and the average state terms. We solve the problem using both the direct method and the fixed-point method in the paper. First, by using the variational method to solve a finite $N$-players game problem, we obtain the necessary and sufficient conditions for the centralized open-loop Nash equilibrium strategy. Subse…

@cmccullough@discuss.systems Signal is centralized just the same as Whatsapp though. What's stopping them from enshittification too? They're already doing closed-source things.

this potential — for AI companies to tweak their chatbots to give pro-Trump answers to appease MAGA — is one of the reasons I'm avoiding LLMs as a political practice. they are inherently extremely centralized, requiring massive resources to crawl data and train. to become dependent on such products is to subject ourselves to the owners' power. this is just one of the ways that can go disastrously wrong

The campaign to make it illegal for ChatGPT to criticize Trump
The conservative pressure campaign against social networks was hugely successful — and now it's coming for AI

Whatever is left of our constitutional rights and liberties exists mostly in the gaps between what the government knows about us. This is the beginning of the end. Soon, when conservatives are in charge, they'll make us follow their stupid, puritanical rules whether we like it or not, and when liberals are in charge, well, they'll make us follow [i]their[/i] stupid, puritanical rules whether we like it or not.
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U.S. Spy Agencies Are Getting a One-Stop Shop to Buy Your Most Sensitive Personal Data
The government wants to build a centralized platform where spy agencies can more easily buy private info about millions of people.

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Qingyi Chen, Ahmed H. Qureshi
https://arxiv.org/abs/2507.13940

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. To address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi…

Pinching-Antenna-based Communications: Spectral Efficiency Analysis and Deployment Strategies
Mengyu Qian, Xidong Mu, Li You, Michail Matthaiou
https://arxiv.org/abs/2507.14831

Pinching-Antenna-based Communications: Spectral Efficiency Analysis and Deployment Strategies
A multiple-waveguide pinching-antenna (PA)-based multi-user communication system is investigated. With a given number of PAs, two deployment strategies are considered, namely the centralized PA deployment, where all PAs are switched between waveguides to serve users in a time-division manner to avail of beamforming gain, and the distributed PA deployment, where a single PA is deployed on each waveguide to simultaneously serve multiple users by leveraging the multiplexing gain. The spectral effi…

Mgmt: We don't have the resources to make our own LLM. Can we just monetize AI prompts?
Business School Student: Hold my beer 🍺
#blockchain

PromptChain: A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. O…

Quotas for scholarship recipients: an efficient race-neutral alternative to affirmative action?
Louis Gleyo
https://arxiv.org/abs/2507.17191 https://arxiv.…

Quotas for scholarship recipients: an efficient race-neutral alternative to affirmative action?
Since 2018, France's centralized higher education platform, Parcoursup, has implemented quotas for scholarship recipients, with program-specific thresholds based on the applicant's composition. Using difference-in-differences methods, I find these quotas enabled scholarship students to access more selective programs, though intention-to-treat effects remain modest (maximum 0.10 SD). Matching methods reveal that the policy improved the scholarship students' waiting list positions relative to tho…

Shelby: Decentralized Storage Designed to Serve
Guy Goren, Andrew Hariri, Timothy D. R. Hartley, Ravi Kappiyoor, Alexander Spiegelman, David Zmick
https://arxiv.org/abs/2506.19233

Shelby: Decentralized Storage Designed to Serve
Existing decentralized storage protocols fall short of the service required by real-world applications. Their throughput, latency, cost-effectiveness, and availability are insufficient for demanding workloads such as video streaming, large-scale data analytics, or AI training. As a result, Web3 data-intensive applications are predominantly dependent on centralized infrastructure. Shelby is a high-performance decentralized storage protocol designed to meet demanding needs. It achieves fast, re…

Pond plumbing dilemna. Any plumbers out there?
The pond will have two strong pumps moving water through the bog filter and around the pool.
One pump will be dedicated to the filter (long black pipe).
The 2nd pump will split duty between the filter and providing water for 3 to 5 emitters in the bottom of the pool and around the edges.
The main piping is black 2” flexible pipe plus some PVC Sched40.
Plumbing Question: Should I reduce diameter once (as pictured badly lol) when branching from the mainline and then have valves for each and long runs of smaller 3/4 or 1/2" line to each sprayer.
Or keep it to 2" throughout and reduce at the outlets?
I would rather have the valve controls centralized than have them spread about where the sprayers are (obv not an option for the bottom ones anyway)
#Pond #DIY #Plumbing #PoolPond #PortAlberni #Home #BackyardPond

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva
https://arxiv.org/abs/2507.15460

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Personalized News Recommendation systems (PNR) have emerged as a solution to information overload by predicting and suggesting news items tailored to individual user interests. However, traditional PNR systems face several challenges, including an overreliance on textual content, common neglect of short-term user interests, and significant privacy concerns due to centralized data storage. This paper addresses these issues by introducing a novel multimodal federated learning-based approach for n…

Internet should not be Centralized.
So join our Discord,
Follow us on Facebook,
Subscribe to our YouTube channel,
Visit our website hosted on AWS behind Cloudflare,
Look at out source code on Github.
Have questions? Mail us at hello57015@gmail.com

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach
Yi Wang, Dawei Qiu, Fei Teng, Goran Strbac
https://arxiv.org/abs/2507.18095

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled power-transport systems. However, previous work solves the coordinated dispatch problem of MPSs and RCs in a centralized manner with the assumption that the communication network is still fully functioning after the event. However, there is growing evidence that ce…

A centralized intelligence database on all Americans is not inherently legal.
It likely breaks the Privacy Act of 1974, the Foreign Intelligence Surveillance Act (FISA), the 1st, 4th and 5th Amendments.
This must be stopped. Contact your Senators and Representative and ask them to stop it. I use @… to call mine.

Trump Taps Palantir to Create Master Database on Every American
Trump’s dystopian plan is already underway.

Price Discovery in Cryptocurrency Markets
Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, Angel Hernando Veciana
https://arxiv.org/abs/2506.08718

Price Discovery in Cryptocurrency Markets
This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book fro…

Tech-project selling these days:
---
Aim: "We will revolutionize the internet by building a centralized, standardized, interoperable, and sustainable platform for the 21st century"
First project step: Let's do some Hackathons!
---
Thank you for nothing!

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
Zahra Mobini, Ahmet Hasim Gokceoglu, Li Wang, Gunnar Peters, Hien Quoc Ngo
https://arxiv.org/abs/2506.14494

Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems
We consider fronthaul-limited generalized zeroforcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multipleantenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral e…

Choreographic Quick Changes: First-Class Location (Set) Polymorphism
Ashley Samuelson, Andrew K. Hirsch, Ethan Cecchetti
https://arxiv.org/abs/2506.10913 h…

Choreographic Quick Changes: First-Class Location (Set) Polymorphism
Choreographic programming is a promising new paradigm for programming concurrent systems where a developer writes a single centralized program that compiles to individual programs for each node. Existing choreographic languages, however, lack critical features integral to modern systems, like the ability of one node to dynamically compute who should perform a computation and send that decision to others. This work addresses this gap with $λ_{QC}$, the first typed choreographic language with \e…

FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
Val Andrei Fajardo, David B. Emerson, Amandeep Singh, Veronica Chatrath, Marcelo Lotif, Ravi Theja, Alex Cheung, Izuki Matsubi
https://arxiv.org/abs/2506.09200

FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
Retrieval-augmented generation (RAG) systems have been shown to be effective in addressing many of the drawbacks of relying solely on the parametric memory of large language models. Recent work has demonstrated that RAG systems can be improved via fine-tuning of their retriever and generator models. In this work, we introduce FedRAG, a framework for fine-tuning RAG systems across centralized and federated architectures. FedRAG supports state-of-the-art fine-tuning methods, offering a simple and…

Chain Table: Protecting Table-Level Data Integrity by Digital Ledger Technology
Feng Yu, Ryan Laird
https://arxiv.org/abs/2507.13932 https://

Chain Table: Protecting Table-Level Data Integrity by Digital Ledger Technology
The rise of blockchain and Digital Ledger Technology (DLT) has gained wide traction. Instead of relying on a traditional centralized data authority, a blockchain system consists of digitally entangled block data shared across a distributed network. The specially designed chain data structure and its consensus mechanism protect blockchain data from being tampered by unauthorized adversaries. However, implementing a full-fledged blockchain system to protect a database can be technically cumbersom…

The reason I’m so fixated on Rudolf Rocker is that he understood authority and centralized control as inherently oppressive structures that suppress individual freedom and the collective ability to self-organize.
He argued that true freedom arises not from legal or political institutions, which often serve to maintain domination, but from the voluntary, free association of people resisting all forms of coercion and hierarchy.
For Rocker, political rights are not granted by govern…

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Wei Wang, Jianke Zhu
https://arxiv.org/abs/2507.04455

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models
The rapid growth of large language models (LLMs) with traditional centralized fine-tuning emerges as a key technique for adapting these models to domain-specific challenges, yielding privacy risks for both model and data owners. One promising solution, called offsite-tuning (OT), is proposed to address these challenges, where a weaker emulator is compressed from the original model and further fine-tuned with adapter to enhance privacy. However, the existing OT-based methods require high computa…

Learning to crawl: benefits and limits of centralized vs distributed control
Luca Gagliardi, Agnese Seminara
https://arxiv.org/abs/2506.02766 https://

Learning to crawl: benefits and limits of centralized vs distributed control
We present a model of a crawler consisting of several suckers distributed along a straight line and connected by springs. Both proprioception and control are binary: the system responds to the elongation vs compression of its springs and the suckers can either adhere or remain idle. A central pattern generator delivers a traveling wave of compression, and the crawler is tasked to learn how to control adhesion to effectively ride the wave in order to crawl. We ask what are the benefits and limit…

(1/3) Please join me today on Zoom at 12pm ET / 11am CT / 10am MT / 9am PT for "Beyond the Algorithmic Feed," a free MediaEd Club webinar for the Media Education Lab:
https://mediaeducationlab.com/index.php/events/beyond-algorithmic-feed
While som…

Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
Alexander Tyurin
https://arxiv.org/abs/2506.23836

Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
We consider centralized distributed optimization in the classical federated learning setup, where $n$ workers jointly find an $\varepsilon$-stationary point of an $L$-smooth, $d$-dimensional nonconvex function $f$, having access only to unbiased stochastic gradients with variance $σ^2$. Each worker requires at most $h$ seconds to compute a stochastic gradient, and the communication times from the server to the workers and from the workers to the server are $τ_{s}$ and $τ_{w}$ seconds per coo…

Faster Multi-Source Reachability and Approximate Distances via Shortcuts, Hopsets and Matrix Multiplication
Michael Elkin, Chhaya Trehan
https://arxiv.org/abs/2507.13470

Faster Multi-Source Reachability and Approximate Distances via Shortcuts, Hopsets and Matrix Multiplication
Given an $n$-vertex $m$-edge digraph $G = (V,E)$ and a subset $S \subseteq V$ of $|S| = n^σ$ (for some $0 \le σ\le 1$) designated sources, the $S \times V$ reachability problem is to compute the sets $\mathcal V_s$ of vertices reachable from $s$, for every $s \in S$. Naive centralized algorithms run BFS/DFS from each source in $O(m \cdot n^σ)$ time or compute $G$'s transitive closure in $\hat O(n^ω)$ time, where $ω\le 2.371552\ldots$ is the matrix multiplication exponent. Thus, t…

Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism
Andreas Mayer
https://arxiv.org/abs/2507.05187 https://

Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism
The proliferation of AI-driven systems presents a fundamental challenge to Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW), often diminishing user agency and failing to account for value pluralism. Current approaches to value alignment, which rely on centralized, top-down definitions, lack the mechanisms for meaningful contestability. This leaves users and communities unable to challenge or shape the values embedded in the systems that govern their digital lives,…

Process Mining on Distributed Data Sources
Maximilian Weisenseel, Julia Andersen, Samira Akili, Christian Imenkamp, Hendrik Reiter, Christoffer Rubensson, Wilhelm Hasselbring, Olaf Landsiedel, Xixi Lu, Jan Mendling, Florian Tschorsch, Matthias Weidlich, Agnes Koschmider
https://arxiv.org/abs/2506.02830

Process Mining on Distributed Data Sources
Major domains such as logistics, healthcare, and smart cities increasingly rely on sensor technologies and distributed infrastructures to monitor complex processes in real time. These developments are transforming the data landscape from discrete, structured records stored in centralized systems to continuous, fine-grained, and heterogeneous event streams collected across distributed environments. As a result, traditional process mining techniques, which assume centralized event logs from enter…

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15233

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Recommendation systems (RS) personalize content by analyzing user preferences, but typically require centralized collection of user data, raising privacy and scalability concerns. Federated Recommendation Systems (FRS) address these issues by enabling distributed, privacy-preserving model training across edge devices, keeping raw data on-device. Although existing FRS frameworks benefit from on-device feature extraction and privacy preservation, they suffer from heterogeneous device capabilities…

Longer Lists Yield Better Matchings
Yuri Faenza, Aapeli Vuorinen
https://arxiv.org/abs/2506.06217 https://arxiv.org/pdf/2506.06217

Longer Lists Yield Better Matchings
Many centralized mechanisms for two-sided matching markets that enjoy strong theoretical properties assume that the planner solicits full information on the preferences of each participating agent. In particular, they expect that participants compile and communicate their complete preference lists over agents from the other side of the market. However, real-world markets are often very large and agents cannot always be expected to even produce a ranking of all options on the other side. It is t…

This https://arxiv.org/abs/2503.04091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalization in Federated Learning: A Conditional Mutual Information Framework
Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized learning. In FL, the generalization error consists of two components: the out-of-sample gap, which measures the gap between the empirical and true risk for participating clients, and the participation gap, which quantifies the risk difference between participating and non-participating clients. In this work, we apply an i…

This https://arxiv.org/abs/2309.14792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Exploiting Local Observations for Robust Robot Learning
While many robotic tasks can be addressed through either centralized single-agent control with full state observation or decentralized multi-agent control, clear criteria for selecting the optimal approach are lacking. This paper presents a comprehensive investigation into how multi-agent reinforcement learning (MARL) with local observations can enhance robustness in complex robotic systems compared to traditional centralized control methods. We provide both theoretical analysis and empirical v…

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
Binbin Xu, G\'erard Dray
https://arxiv.org/abs/2507.03122 h…

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from the MIMIC-IV dataset. Unlike previous approaches that rely on centralized training or fine-tuned large language models, we propose a lightweight and scalable pipeline combining frozen text embeddings with simple multilayer perceptron (MLP) classifiers. This design offers a privacy-preserving and deployment-efficient alternative for clinical NLP app…

Self-Boosted Weight-Constrained FxLMS: A Robustness Distributed Active Noise Control Algorithm Without Internode Communication
Junwei Ji, Dongyuan Shi, Zhengding Luo, Boxiang Wang, Woon-Seng Gan
https://arxiv.org/abs/2507.12045

Self-Boosted Weight-Constrained FxLMS: A Robustness Distributed Active Noise Control Algorithm Without Internode Communication
Compared to the conventional centralized multichannel active noise control (MCANC) algorithm, which requires substantial computational resources, decentralized approaches exhibit higher computational efficiency but typically result in inferior noise reduction performance. To enhance performance, distributed ANC methods have been introduced, enabling information exchange among ANC nodes; however, the resulting communication latency often compromises system stability. To overcome these limitation…

This https://arxiv.org/abs/2412.09433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Solving Multiagent Path Finding on Highly Centralized Networks
The Mutliagent Path Finding (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destinations as soon as possible, but without colliding with each other. We aim to minimize the maximum time any agent takes to reach their goal, ensuring optimal path length. In this work, we complement a recent thread of results that aim to systematically study the algorithmic behavior of this problem, through the paramet…

Arbitrage on Decentralized Exchanges
Xue Dong He, Chen Yang, Yutian Zhou
https://arxiv.org/abs/2507.08302 https://arxiv.org/pdf/2507.…

Arbitrage on Decentralized Exchanges
Decentralized exchanges (DEXs) are alternative venues to centralized exchanges (CEXs) for trading cryptocurrencies and have become increasingly popular. An arbitrage opportunity arises when the exchange rate of two cryptocurrencies in a DEX differs from that in a CEX. Arbitrageurs can then trade on the DEX and CEX to make a profit. Trading on the DEX incurs a gas fee, which determines the priority of the trade being executed. We study a gas-fee competition game between two arbitrageurs who maxi…

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks
Eyad Gad, Gad Gad, Mostafa M. Fouda, Mohamed I. Ibrahem, Muhammad Ismail, Zubair Md Fadlullah
https://arxiv.org/abs/2507.02021

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks
With the rise of Software-Defined Networking (SDN) for managing traffic and ensuring seamless operations across interconnected devices, challenges arise when SDN controllers share infrastructure with deep learning (DL) workloads. Resource contention between DL training and SDN operations, especially in latency-sensitive IoT environments, can degrade SDN's responsiveness and compromise network performance. Federated Learning (FL) helps address some of these concerns by decentralizing DL training…

Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest
Fei Wu, Danning Sui, Thomas Thiery, Mallesh Pai
https://arxiv.org/abs/2507.13023

Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest
This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges (CEX-DEX) on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to estimate arbitrage revenue without knowing traders' actual behaviors on CEX. Leveraging an extensive dataset spanning 19 months from August 2023 to March 2025, we estimate a total of 233.8M USD extracted …

A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization
A. A. Vyguzov, F. S. Stonyakin
https://arxiv.org/abs/2507.05669

A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization
We study the Frank-Wolfe algorithm for constrained optimization problems with relatively smooth and relatively strongly convex objectives. Building upon our previous work, we propose a fully adaptive variant of the Frank-Wolfe method that dynamically adjusts the step size based on both the relative smoothness constant and the Triangle Scaling Exponent (TSE) of the Bregman divergence. Our method does not require prior knowledge of the function parameters and guarantees convergence using only loc…

Minimum Clustering of Matrices Based on Phase Alignment
Honghao Wu, Kemi Ding, Li Qiu
https://arxiv.org/abs/2507.13678 https://arxiv.…

Minimum Clustering of Matrices Based on Phase Alignment
Coordinating multi-agent systems requires balancing synchronization performance and controller implementation costs. To this end, we classify agents by their intrinsic properties, enabling each group to be controlled by a uniform controller and thus reducing the number of unique controller types required. Existing centralized control methods, despite their capability to achieve high synchronization performance with fewer types of controllers, suffer from critical drawbacks such as limited scala…

Edge Intelligence with Spiking Neural Networks
Shuiguang Deng, Di Yu, Changze Lv, Xin Du, Linshan Jiang, Xiaofan Zhao, Wentao Tong, Xiaoqing Zheng, Weijia Fang, Peng Zhao, Gang Pan, Schahram Dustdar, Albert Y. Zomaya
https://arxiv.org/abs/2507.14069

Edge Intelligence with Spiking Neural Networks
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditional deep learning models require significant computational resources and centralized data management, the resulting latency, bandwidth consumption, and privacy concerns have exposed critical limitations in cloud-centric paradigms. Brain-inspired computing, particularly Spiking Neural Networks (SNNs), offers a promisin…

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Hang Lv, Sheng Liang, Hao Wang, Hongchao Gu, Yaxiong Wu, Wei Guo, Defu Lian, Yong Liu, Enhong Chen
https://arxiv.org/abs/2507.04756

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Personalized text generation has become crucial for adapting language models to diverse and evolving users' personal context across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment, they struggle to achieve real-time adaptation under resource constraints inherent to personal devices. This limitation creates a dilemma: large cloud-based models lack access to localized user-specific information, while small …

Rudolf Rocker (1873–1958) was a prominent German anarchist writer and activist, best known for his advocacy of anarcho-syndicalism.
Rocker argued that genuine liberation cannot be achieved through authoritarian systems or centralized state power.
Rocker was deeply critical of the Bolshevik regime in Russia, viewing it as a betrayal of the ideals of freedom and workers’ self-management.
He believed that the Bolsheviks simply replaced one form of oppression with another, demo…

LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
Ajay Shankar, Keisuke Okumura, Amanda Prorok
https://arxiv.org/abs/2507.11464 https:/…

LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full-horizon planner for computing collision- and deadlock-free paths rapidly, leveraging recent advances in multi-agent pathfinding (MAPF), and (ii) dynamics-aware, robot-wise optimal trajectory controllers that ensure all robots in…

Decision-making in light-trapped slime molds involves active mechanical processes
Lisa Schick, Emily Eichenlaub, Fabian Drexel, Alexander Mayer, Siyu Chen, Marcus Roper, Karen Alim
https://arxiv.org/abs/2506.12803

Decision-making in light-trapped slime molds involves active mechanical processes
Decision-making is the process of selecting an action among alternatives, allowing biological and artificial systems to navigate complex environments and optimize behavior. While neural systems rely on neuron-based sensory processing and evaluation, decision-making also occurs in organisms without a centralized organizing unit, such as the unicellular slime mold \textit{Physarum polycephalum}. Unlike neural systems, P. polycephalum relies on rhythmic peristaltic contractions to drive internal f…

scikit-package -- software packaging standards and roadmap for sharing reproducible scientific software
S. Lee, C. Myers, A. Yang, T. Zhang, S. J. L. Billinge
https://arxiv.org/abs/2507.03328

scikit-package -- software packaging standards and roadmap for sharing reproducible scientific software
Scientific advancement relies on the ability to share and reproduce results. When data analysis or calculations are carried out using software written by scientists there are special challenges around code versions, quality and code sharing. scikit-package provides a roadmap to facilitate code reuse and sharing with minimal effort through tutorials coupled with automated and centralized reusable workflows. The goal of the project is to provide pedagogical and practical tools for scientists who …

An advanced reliability reserve incentivizes flexibility investments while safeguarding the electricity market
Franziska Klaucke, Karsten Neuhoff, Alexander Roth, Wolf-Peter Schill, Leon Stolle
https://arxiv.org/abs/2506.14664

An advanced reliability reserve incentivizes flexibility investments while safeguarding the electricity market
To ensure security of supply in the power sector, many countries are already using or discussing the introduction of capacity mechanisms. Two main types of such mechanisms include capacity markets and capacity reserves. Simultaneously, the expansion of variable renewable energy sources increases the need for power sector flexibility, for which there are promising yet often under-utilized options on the demand side. In this paper, we analyze how a centralized capacity market and an advanced reli…

In-liquid Plasma Catalysis for Nitrogen Reduction
P. Grosse, J. L. Gembus, F. Landwehr, A. R. Silva Olaya, D. Escalera-L\'opez, N. Bibinov, A. R. Gibson, S. Z. Oener, B. Roldan Cuenya
https://arxiv.org/abs/2506.21791

In-liquid Plasma Catalysis for Nitrogen Reduction
Ammonia, a crucial component in fertilizers and fuels, is currently produced by the energy-intensive Haber-Bosch process. However, due to the high upfront investments required for large-scale centralized production, alternative routes for small-scale applications are being sought. We integrate in-liquid plasma with electrocatalysis for ammonia generation via the nitrogen reduction reaction (NRR). Among materials tested, platinum emerged as the most stable and active catalyst - it evolves hydrog…

Heatables: Effects of Infrared-LED-Induced Ear Heating on Thermal Perception, Comfort, and Cognitive Performance
Valeria Zitz, Michael K\"uttner, Jonas Hummel, Michael T. Knierim, Michael Beigl, Tobias R\"oddiger
https://arxiv.org/abs/2506.02714

Heatables: Effects of Infrared-LED-Induced Ear Heating on Thermal Perception, Comfort, and Cognitive Performance
Maintaining thermal comfort in shared indoor environments remains challenging, as centralized HVAC systems are slow to adapt and standardized to group norms. Cold exposure not only reduces subjective comfort but can impair cognitive performance, particularly under moderate to severe cold stress. Personal Comfort Systems (PCS) have shown promise by providing localized heating, yet many designs target distal body parts with low thermosensitivity and often lack portability. In this work, we invest…

Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Armin Lederer, Erfaun Noorani, Andreas Krause
https://arxiv.org/abs/2506.07347

Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sensitive safety filter for discrete-time multi-agent systems with uncertain dynamics that leverages control barrier functions (CBFs) defined through value functions. Our approach relies on centralized risk-sensitive safety conditions based on exponential risk operators to ensure robustness against model uncertainties. W…

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Ming Wang, Zhaoyang Duan, Dong Xue, Fangzhou Liu, Zhongheng Zhang
https://arxiv.org/abs/2507.08050 https://arxiv.org/pdf/2507.08050 https://arxiv.org/html/2507.08050
arXiv:2507.08050v1 Announce Type: new
Abstract: The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions.
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Privacy-preserving and reward-based mechanisms of proof of engagement
Matteo Marco Montanari, Alessandro Aldini
https://arxiv.org/abs/2506.12523 https://…

Privacy-preserving and reward-based mechanisms of proof of engagement
Proof-of-Attendance (PoA) mechanisms are typically employed to demonstrate a specific user's participation in an event, whether virtual or in-person. The goal of this study is to extend such mechanisms to broader contexts where the user wishes to digitally demonstrate her involvement in a specific activity (Proof-of-Engagement, PoE). This work explores different solutions, including DLTs as well as established technologies based on centralized systems. The main aspects we consider include the l…

Fast Distributed Nash Equilibrium Seeking in Monotone Games
Tatiana Tatarenko, Angelia Nedich
https://arxiv.org/abs/2507.11703 https://

Fast Distributed Nash Equilibrium Seeking in Monotone Games
This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with merely monotone and restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method presented in [5] to solve variational inequalities, we develop the algorithm converging to Nash equilibria in games, where players have no access to the full information but are able to communicate with neighbors over some communication graph. The convergenc…

Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition
Tao Zhong, Mengzhe Geng, Shujie Hu, Guinan Li, Xunying Liu
https://arxiv.org/abs/2506.11069

Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition
Accurate recognition of dysarthric and elderly speech remains challenging to date. While privacy concerns have driven a shift from centralized approaches to federated learning (FL) to ensure data confidentiality, this further exacerbates the challenges of data scarcity, imbalanced data distribution and speaker heterogeneity. To this end, this paper conducts a systematic investigation of regularized FL techniques for privacy-preserving dysarthric and elderly speech recognition, addressing differ…

Resilient Time-Sensitive Networking for Industrial IoT: Configuration and Fault-Tolerance Evaluation
Mohamed Seliem, Dirk Pesch, Utz Roedig, Cormac Sreenan
https://arxiv.org/abs/2507.11250

Resilient Time-Sensitive Networking for Industrial IoT: Configuration and Fault-Tolerance Evaluation
Time-Sensitive Networking (TSN) is increasingly adopted in industrial systems to meet strict latency, jitter, and reliability requirements. However, evaluating TSN's fault tolerance under realistic failure conditions remains challenging. This paper presents IN2C, a modular OMNeT++/INET-based simulation framework that models two synchronized production cells connected to centralized infrastructure. IN2C integrates core TSN features, including time synchronization, traffic shaping, per-stream fil…

Understanding Community-Level Blocklists in Decentralized Social Media
Owen Xingjian Zhang, Sohyeon Hwang, Yuhan Liu, Manoel Horta Ribeiro, Andr\'es Monroy-Hern\'andez
https://arxiv.org/abs/2506.05522

Understanding Community-Level Blocklists in Decentralized Social Media
Community-level blocklists are key to content moderation practices in decentralized social media. These blocklists enable moderators to prevent other communities, such as those acting in bad faith, from interacting with their own -- and, if shared publicly, warn others about communities worth blocking. Prior work has examined blocklists in centralized social media, noting their potential for collective moderation outcomes, but has focused on blocklists as individual-level tools. To understand h…

The AI Shadow War: SaaS vs. Edge Computing Architectures
Rhea Pritham Marpu, Kevin J McNamara, Preeti Gupta
https://arxiv.org/abs/2507.11545 https://

The AI Shadow War: SaaS vs. Edge Computing Architectures
The very DNA of AI architecture presents conflicting paths: centralized cloud-based models (Software-as-a-Service) versus decentralized edge AI (local processing on consumer devices). This paper analyzes the competitive battleground across computational capability, energy efficiency, and data privacy. Recent breakthroughs show edge AI challenging cloud systems on performance, leveraging innovations like test-time training and mixture-of-experts architectures. Crucially, edge AI boasts a 10,000x…

Modeling Wallet-Level Behavioral Shifts Post-FTX Collapse: An XAI-Driven GLM Study on Ethereum Transactions
Benjamin Gillen, Rashmi Ranjan Bhuyan, Gourab Mukherjee, Austin Pollok
https://arxiv.org/abs/2507.08455

Modeling Wallet-Level Behavioral Shifts Post-FTX Collapse: An XAI-Driven GLM Study on Ethereum Transactions
The Ethereum blockchain plays a central role in the broader cryptocurrency ecosystem, enabling a wide range of financial activity through the use of smart contracts. This paper investigates how individual Ethereum wallets responded to the collapse of FTX, one of the largest centralized cryptocurrency exchanges. Moving beyond price-based event studies, we adopt a bottom-up approach using granular wallet-level data. We construct a representative sample of Ethereum addresses and analyze their tran…

Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization
Yang Zhao, Yue Xiu, Chengxiao Dai, Ning Wei, Dusit Niyato
https://arxiv.org/abs/2506.00011

Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization
Large language model (LLM) training in 6G networks faces stringent latency and energy constraints while operating over bandwidth-limited wireless links. A commonly adopted workflow separates training into a centralized pre-training phase and a federated fine-tuning phase on domain-specific data; however, over-the-air (OTA) gradient aggregation during fine-tuning remains vulnerable to fading and interference. This study explores the integration of movable antennas (MAs), whose element positions …

Horus: A Protocol for Trustless Delegation Under Uncertainty
David Shi, Kevin Joo
https://arxiv.org/abs/2507.00631 https://arxiv.org/…

Horus: A Protocol for Trustless Delegation Under Uncertainty
Correctness is an emergent property of systems where exposing error is cheaper than committing it. In dynamic, low-trust environments, autonomous AI agents benefit from delegating work to sub-agents, yet correctness cannot be assured through upfront specification or centralized oversight. We propose a protocol that enforces correctness through collateralized claims in a recursive verification game. Tasks are published as intents, and solvers compete to fulfill them. Selected solvers carry out t…

This https://arxiv.org/abs/2505.04947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prot…

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
Davide Maffiola, Stefano Longari, Michele Carminati, Mara Tanelli, Stefano Zanero
https://arxiv.org/abs/2506.10665

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the…

On the Linear Programming Model for Dynamic Stochastic Matching and Its Application on Pricing
Junlin Chen, Chiwei Yan, Hai Jiang
https://arxiv.org/abs/2506.09924

On the Linear Programming Model for Dynamic Stochastic Matching and Its Application on Pricing
Important pricing problems in centralized matching markets -- such as carpooling and food delivery platforms -- often exhibit a bi-level structure. At the upper level, the platform sets prices for different types of agents (e.g., riders with various origins and destinations, or delivery orders from diverse restaurants to customer locations). The lower level then matches converted agents to minimize operational costs, such as pooling riders with similar itineraries into the same vehicle or conso…

INTACT: Compact Storage of Data Streams in Mobile Devices to Unlock User Privacy at the Edge
R\'emy Raes (SPIRALS), Olivier Ruas (SPIRALS), Adrien Luxey-Bitri (SPIRALS), Romain Rouvoy (SPIRALS)
https://arxiv.org/abs/2506.21998

INTACT: Compact Storage of Data Streams in Mobile Devices to Unlock User Privacy at the Edge
Data streams produced by mobile devices, such as smartphones, offer highly valuable sources of information to build ubiquitous services. Such data streams are generally uploaded and centralized to be processed by third parties, potentially exposing sensitive personal information. In this context, existing protection mechanisms, such as Location Privacy Protection Mechanisms (LPPMs), have been investigated. Alas, none of them have actually been implemented, nor deployed in real-life, in mobile d…

This https://arxiv.org/abs/2505.21388 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

DeSocial: Blockchain-based Decentralized Social Networks
Web 2.0 social platforms are inherently centralized, with user data and algorithmic decisions controlled by the platform. However, users can only passively receive social predictions without being able to choose the underlying algorithm, which limits personalization. Fortunately, with the emergence of blockchain, users are allowed to choose algorithms that are tailored to their local situation, improving prediction results in a personalized way. In a blockchain environment, each user possesses …

This https://arxiv.org/abs/2506.03940 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Depermissioning Web3: a Permissionless Accountable RPC Protocol for Blockchain Networks
In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain network. In practice, the serving layer is dominated by a small number of centralized services ("node providers") that offer permissioned access to RPC endpoints. Clients register with these providers …

Distributed component-level modeling and control of energy dynamics in electric power systems
Hiya Gada, Rupamathi Jaddivada, Marija Ilic
https://arxiv.org/abs/2506.08861

Distributed component-level modeling and control of energy dynamics in electric power systems
The widespread deployment of power electronic-based technologies is transforming modern power systems into fast, nonlinear, and heterogeneous systems. Conventional modeling and control approaches, rooted in quasi-static analysis and centralized control, are inadequate for these converter-dominated systems, which operate on fast timescales and involve proprietary models of diverse components. This paper adopts and extends a previously introduced energy space modeling framework grounded in energy…

Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Maria Voreakou, George Kousiouris, Mara Nikolaidou
https://arxiv.org/abs/2506.10693

Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and the Internet of Things, messaging systems have become an integral and mainstream part of modern computing infrastructures, carrying out significant workload in a majority of applications. In this paper, we describe an experimental process to explore energy-ba…

This https://arxiv.org/abs/2504.00587 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
The rapid advancement of large language models (LLMs) has enabled the development of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, existing systems often rely on centralized coordination, leading to scalability bottlenecks, reduced adaptability, and single points of failure. Privacy and proprietary knowledge concerns further hinder cross-organizational collaboration, resulting in siloed expertise. We propose AgentNet, a decentralized, Retrieval-Augme…

Design and Analysis of Power Consumption Models for Open-RAN Architectures
Urooj Tariq, Rishu Raj, Dan Kilper
https://arxiv.org/abs/2505.24552 https://

Design and Analysis of Power Consumption Models for Open-RAN Architectures
The open radio access network (O-RAN) Alliance developed an architecture and specifications for open and disaggregated cellular networks including many elements that are being widely adopted and implemented in both commercial and research networks. In this paper, we develop transaction-based power consumption models of a centralized O-RAN architecture based on commercial hardware and considering the full end-to-end data path from the radio unit to the data center. We focus on recent fanout limi…

This https://arxiv.org/abs/2506.02841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with d…

Fast-Converging Distributed Signal Estimation in Topology-Unconstrained Wireless Acoustic Sensor Networks
Paul Didier, Toon van Waterschoot, Simon Doclo, J\"org Bitzer, Marc Moonen
https://arxiv.org/abs/2506.02797

Fast-Converging Distributed Signal Estimation in Topology-Unconstrained Wireless Acoustic Sensor Networks
This paper focuses on distributed signal estimation in topology-unconstrained wireless acoustic sensor networks (WASNs) where sensor nodes only transmit fused versions of their local sensor signals. For this task, the topology-independent (TI) distributed adaptive node-specific signal estimation (DANSE) algorithm (TI-DANSE) has previously been proposed. It converges towards the centralized signal estimation solution in non-fully connected and time-varying network topologies. However, the applic…

Decentralized COVID-19 Health System Leveraging Blockchain
Lingsheng Chen, Shipeng Ye, Xiaoqi Li
https://arxiv.org/abs/2506.02674 https://

Decentralized COVID-19 Health System Leveraging Blockchain
With the development of the Internet, the amount of data generated by the medical industry each year has grown exponentially. The Electronic Health Record (EHR) manages the electronic data generated during the user's treatment process. Typically, an EHR data manager belongs to a medical institution. This traditional centralized data management model has many unreasonable or inconvenient aspects, such as difficulties in data sharing, and it is hard to verify the authenticity and integrity of the…

Malicious node aware wireless multi hop networks: a systematic review of the literature and recommendations for future research
Shahram Pourdehghan, Nahideh Derakhshanfard
https://arxiv.org/abs/2506.05742

Malicious node aware wireless multi hop networks: a systematic review of the literature and recommendations for future research
Wireless communication provides great advantages that are not available through their wired counterparts such as flexibility, ease of deployment and use, cost reductions, and convenience. Wireless multi-hop networks (WMN) do not have any centralized management infrastructure. Wireless multi-hop networks have many benefits since proposed. In such networks when a node wants to send a packet to a destination where is not in the transmission range, depend on some intermediate nodes. In this type of…

Explaining Sustained Blockchain Decentralization with Quasi-Experiments: Resource Flexibility of Consensus Mechanisms
Harang Ju, Madhav Kumar, Ehsan Valavi, Sinan Aral
https://arxiv.org/abs/2505.24663

Explaining Sustained Blockchain Decentralization with Quasi-Experiments: Resource Flexibility of Consensus Mechanisms
Decentralization is a fundamental design element of the Web3 economy. Blockchains and distributed consensus mechanisms are touted as fault-tolerant, attack-resistant, and collusion-proof because they are decentralized. Recent analyses, however, find some blockchains are decentralized, others are centralized, and that there are trends towards both centralization and decentralization in the blockchain economy. Despite the importance and variability of decentralization across blockchains, we still…

Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention
Gordon Y. Liao, Ziming Zeng, Mira Belenkiy, Jacob Hirshman
https://arxiv.org/abs/2505.24284

Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention
This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities (EAIs)", wallets with direct transaction connections to centralized exchanges. Recognizing the limitations of traditional approaches like blocklisting (reactive, slow) and strict allow listing (privacy-invasive, adoption barriers), we propose…

Sensor Fusion Methods for Gaussian Mixture Models
Ishan Paranjape, Islam Hussein, Jeremy Murray-Krezan, Sean Phillips, Suman Chakravorty
https://arxiv.org/abs/2506.00383

Sensor Fusion Methods for Gaussian Mixture Models
Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of a set of targets with their immediate neighbors without the need of a centralized node or layer. We present decentralized consensus-based fusion techniques for a system whose target prior estimates are a weighted mixture of Gaussian probability density functions (PDFs) for the following cases: 1) in which all agents …

Navigating the Edge-Cloud Continuum: A State-of-Practice Survey
Loris Belcastro, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, Paolo Trunfio
https://arxiv.org/abs/2506.02003

Navigating the Edge-Cloud Continuum: A State-of-Practice Survey
The edge-cloud continuum has emerged as a transformative paradigm that meets the growing demand for low-latency, scalable, end-to-end service delivery by integrating decentralized edge resources with centralized cloud infrastructures. Driven by the exponential growth of IoT-generated data and the need for real-time responsiveness, this continuum features multi-layered architectures. However, its adoption is hindered by infrastructural challenges, fragmented standards, and limited guidance for d…

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
Utku Demir, Yalin E. Sagduyu, Tugba Erpek, Hossein Jafari, Sastry Kompella, Mengran Xue
https://arxiv.org/abs/2505.23706

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
In connected and autonomous vehicles, machine learning for safety message classification has become critical for detecting malicious or anomalous behavior. However, conventional approaches that rely on centralized data collection or purely local training face limitations due to the large scale, high mobility, and heterogeneous data distributions inherent in inter-vehicle networks. To overcome these challenges, this paper explores Distributed Federated Learning (DFL), whereby vehicles collaborat…

Momentum-based Accelerated Algorithm for Distributed Optimization under Sector-Bound Nonlinearity
Mohammadreza Doostmohammadian, Hamid R. Rabiee
https://arxiv.org/abs/2506.22855

Momentum-based Accelerated Algorithm for Distributed Optimization under Sector-Bound Nonlinearity
Distributed optimization advances centralized machine learning methods by enabling parallel and decentralized learning processes over a network of computing nodes. This work provides an accelerated consensus-based distributed algorithm for locally non-convex optimization using the gradient-tracking technique. The proposed algorithm (i) improves the convergence rate by adding momentum towards the optimal state using the heavy-ball method, while (ii) addressing general sector-bound nonlinearities…

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
Beichen Huang, Ran Cheng, Kay Chen Tan
https://arxiv.org/abs/2506.02049 https:…

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent coding agents, each proposing edits to a shared codebase without centralized coordination, explicit message passing, or shared memory. Instead, all coordination emerges through a Git-based phylogenetic graph that tracks the full version lineage and enables agents to asynchronously read from and write to the evolving cod…

Performance Measurements in the AI-Centric Computing Continuum Systems
Praveen Kumar Donta, Qiyang Zhang, Schahram Dustdar
https://arxiv.org/abs/2506.22884

Performance Measurements in the AI-Centric Computing Continuum Systems
Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud, edge, Internet of Things (IoT), and mobile platforms work together to support a wide range of applications. Recently, the emergence of Generative AI and large language models has further intensified the demand for computational resources across this continuum…

Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
Tom Danino, Nahum Shimkin
https://arxiv.org/abs/2506.02841 https://

Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with d…