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

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
J
Java Code Geeks
I
InfoQ
V
Visual Studio Blog
M
MIT News - Artificial intelligence
H
Help Net Security
博客园_首页
Blog — PlanetScale
Blog — PlanetScale
F
Fortinet All Blogs
Apple Machine Learning Research
Apple Machine Learning Research
人人都是产品经理
人人都是产品经理
G
Google Developers Blog
A
About on SuperTechFans
腾讯CDC
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
Last Week in AI
Last Week in AI
小众软件
小众软件
aimingoo的专栏
aimingoo的专栏
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
云风的 BLOG
云风的 BLOG
S
SegmentFault 最新的问题
WordPress大学
WordPress大学

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
Formal Verification of Blockchain Byzantine Fault Tolerance
Pierre Tholoniat, Vincent Gramoli · 2019-09-17 · via cs.DC updates on arXiv.org

To implement a blockchain, the trend is now to integrate a non-trivial Byzantine fault tolerant consensus algorithm instead of the seminal idea of waiting to receive blocks to decide upon the longest branch. After a decade of existence, blockchains trade now large amounts of valuable assets and a simple disagreement could lead to disastrous losses. Unfortunately, Byzantine consensus solutions used in blockchains are at best proved correct "by hand" as we are not aware of any of them having been formally verified. In this paper, we propose two contributions: (i) we illustrate the severity of the problem by listing six vulnerabilities of blockchain consensus including two new counter-examples; (ii) we then formally verify two Byzantine fault tolerant components of Red Belly Blockchain using the ByMC model checker. First, we specify a simple broadcast primitive in 116 lines of code that is verified in 40 seconds on a 2-core Intel machine. Then, we specify a blockchain consensus algorithm in 276 lines of code that is verified in 17 minutes on a 64-core AMD machine using MPI. To conclude, we argue that it has now become both relatively simple and crucial to formally verify the correctness of blockchain consensus protocols.