Latest Articles for Distributed Systems

Discover how distributed algorithms enhance security and efficiency in communication networks.

A new method enhances decision-making in distributed systems with limited communication.

Learn how new algorithms tackle consensus challenges in distributed computing.

A look at organizing partially ordered events in computer science.

Research on efficient data recovery in distributed storage systems facing server failures.

A new method enhances the analysis of multi-trace properties in computing systems.

A new framework for federated learning adapts to shifting data while ensuring privacy.

This research tackles the tradeoff between access efficiency and data redundancy.

This article presents a method for efficient view synchronization among processes in systems.

A look into BNRA and the coverability problem for agent coordination.

A look into Narwhal and Bullshark for faster consensus and reliability.

New methods streamline the creation of communication protocols, boosting efficiency and correctness.

A new method improves spatial data partitioning for faster processing.

This paper presents a robust solution for reliable communication in distributed systems.

A look at Clip21's role in enhancing differential privacy during model training.

A new framework tackles challenges of Byzantine agents in distributed optimization.

Research on algorithms for finding shortest paths in distributed graph models.

Analysis of agent interactions and meeting times in non-atomic random walks.

Exploring strategies to find the best solution for combined functions in optimization.

A look at causal consistency in distributed data systems using erasure coding.

New algorithms enhance model performance in federated learning through effective client clustering.

A new approach enhances privacy while optimizing collaborative agent performance.

MementoHash offers efficient data distribution across nodes in cloud environments.

A new framework improves learning from limited data in federated environments.

A new deterministic algorithm for (degree + 1)-list coloring in distributed systems.

A look at decentralized deep learning approaches that prioritize data privacy.

A study on learning methods for broadcast protocols in complex systems.

Learn how minimally reactive networks facilitate consensus among connected systems.

Examining the risks of data poisoning in SplitFed Learning applications.

A new framework enhances distributed optimization for complex non-convex problems.

FedInit improves device collaboration in federated learning by addressing client drift.

FedDec enhances federated learning by enabling direct device communication for better model training.

Examining pretrained transformers for multitask learning and communication efficiency in federated settings.

A look into safety analysis in distributed systems using non-blocking rendezvous.

SelSync balances fast training and model accuracy in distributed deep learning.

A novel system for managing transactions in leaderless distributed ledgers without high fees.

Discover a new log system designed to maintain order in unpredictable situations.

This study analyzes how packet loss affects performance in Split-Federated Learning.

A method to efficiently recycle consensus objects in distributed systems facing Byzantine failures.

Strategies to enhance efficiency in distributed computing.