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

freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
爱范儿
爱范儿
WordPress大学
WordPress大学
博客园 - 三生石上(FineUI控件)
J
Java Code Geeks
Vercel News
Vercel News
aimingoo的专栏
aimingoo的专栏
T
Tailwind CSS Blog
罗磊的独立博客
B
Blog
博客园_首页
A
About on SuperTechFans
有赞技术团队
有赞技术团队
V
V2EX
U
Unit 42
I
InfoQ
IT之家
IT之家
博客园 - 司徒正美
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Stack Overflow Blog
Stack Overflow Blog
The Cloudflare Blog
H
Help Net Security

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
From Byzantine Failures to Crash Failures in Message-Pass...
Damien Imbs, Michel Raynal, Julien Stainer · 2015-10-30 · via cs.DC updates on arXiv.org

The BG-simulation is a powerful reduction algorithm designed for asynchronous read/write crash-prone systems. It allows a set of $(t+1)$ asynchronous sequential processes to wait-free simulate (i.e., despite the crash of up to $t$ of them) an arbitrary number $n$ of processes under the assumption that at most $t$ of them may crash. The BG simulation shows that, in read/write systems, the crucial parameter is not the number $n$ of processes, but the upper bound $t$ on the number of process crashes. The paper extends the concept of BG simulation to asynchronous message-passing systems prone to Byzantine failures. Byzantine failures are the most general type of failure: a faulty process can exhibit any arbitrary behavior. Because of this, they are also the most difficult to analyze and to handle algorithmically. The main contribution of the paper is a signature-free reduction of Byzantine failures to crash failures. Assuming $t<\min(n',n/3)$, the paper presents an algorithm that simulates a system of $n'$ processes where up to $t$ may crash, on top of a basic system of $n$ processes where up to $t$ may be Byzantine. While topological techniques have been used to relate the computability of Byzantine failure-prone systems to that of crash failure-prone ones, this simulation is the first, to our knowledge, that establishes this relation directly, in an algorithmic way. In addition to extending the basic BG simulation to message-passing systems and failures more severe than process crashes, being modular and direct, this simulation provides us with a deeper insight in the nature and understanding of crash and Byzantine failures in the context of asynchronous message-passing systems. Moreover, it also allows crash-tolerant algorithms, designed for asynchronous read/write systems, to be executed on top of asynchronous message-passing systems prone to Byzantine failures.