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

V
Visual Studio Blog
Stack Overflow Blog
Stack Overflow Blog
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
L
LangChain Blog
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
Y
Y Combinator Blog
月光博客
月光博客
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
F
Fortinet All Blogs
A
About on SuperTechFans
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
小众软件
小众软件
H
Help Net Security
Last Week in AI
Last Week in AI
B
Blog RSS Feed
宝玉的分享
宝玉的分享
N
Netflix TechBlog - Medium
博客园 - 叶小钗
The GitHub Blog
The GitHub Blog

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
StairDag: Cross-DAG Validation For Scalable BFT Consensus
Quan Nguyen, Andre Cronje, Michael Kong, Alex Kampa, George Samm · 2019-08-29 · via cs.DC updates on arXiv.org

This paper introduces a new consensus protocol, so-called \emph{\stair}, for fast consensus in DAG-based trustless system. In \stair, we propose a new approach to creating local block DAG, namely \emph{x-DAG} (cross-DAG), on each node. \emph{\stair} protocol is based on our Proof-of-Stake StakeDag framework \cite{stakedag} that distinguishes participants into users and validators by their stake. Both users and validators can create and validate event blocks. Unlike StakeDag's DAG, x-DAG ensures that each new block has to have parent blocks from both Users and Validators to achieve more safety and liveness. Our protocol leverages a pool of validators to expose more validating power to new blocks for faster consensus in a leaderless asynchronous system. Further, our framework allows participants to join as observers / monitors, who can retrieve DAG for post-validation, but do not participate in onchain validation.