惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

爱范儿
爱范儿
量子位
人人都是产品经理
人人都是产品经理
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - Franky
Recent Announcements
Recent Announcements
A
About on SuperTechFans
Microsoft Security Blog
Microsoft Security Blog
N
Netflix TechBlog - Medium
H
Help Net Security
博客园 - 三生石上(FineUI控件)
博客园 - 司徒正美
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
MyScale Blog
MyScale Blog
The Cloudflare Blog
S
SegmentFault 最新的问题
H
Hackread – Cybersecurity News, Data Breaches, AI and More
J
Java Code Geeks
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
P
Proofpoint News Feed
宝玉的分享
宝玉的分享
Vercel News
Vercel News

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
PALE: Partially Asynchronous Agile Leader Election
Bronislav Sidik, Rami Puzis, Polina Zilberman, Yuval Elovici · 2018-01-11 · via cs.DC updates on arXiv.org

Many tasks executed in dynamic distributed systems, such as sensor networks or enterprise environments with bring-your-own-device policy, require central coordination by a leader node. In the past it has been proven that distributed leader election in dynamic environments with constant changes and asynchronous communication is not possible. Thus, state-of-the-art leader election algorithms are not applicable in asynchronous environments with constant network changes. Some algorithms converge only after the network stabilizes (an unrealistic requirement in many dynamic environments). Other algorithms reach consensus in the presence of network changes but require a global clock or some level of communication synchronization. Determining the weakest assumptions, under which leader election is possible, remains an unresolved problem. In this study we present a leader election algorithm that operates in the presence of changes and under weak (realistic) assumptions regarding message delays and regarding the clock drifts of the distributed nodes. The proposed algorithm is self-sufficient, easy to implement and can be extended to support multiple regions, self-stabilization, and wireless ad-hoc networks. We prove the algorithm's correctness and provide a complexity analysis of the time, space, and number of messages required to elect a leader.