Tootfinder

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…

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…

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.

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…

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…

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…

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…

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…

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…

(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…

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…

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…

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 …

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…

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…

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…

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…

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…

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 …

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.

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…

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 …

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…

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…

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.

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…