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

T
Tailwind CSS Blog
博客园 - 【当耐特】
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - Franky
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 三生石上(FineUI控件)
B
Blog
有赞技术团队
有赞技术团队
J
Java Code Geeks
云风的 BLOG
云风的 BLOG
Recent Announcements
Recent Announcements
Jina AI
Jina AI
Vercel News
Vercel News
博客园 - 叶小钗
Hugging Face - Blog
Hugging Face - Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
F
Fortinet All Blogs
The Cloudflare Blog
V
V2EX
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园_首页
腾讯CDC

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
ALDER: Unlocking blockchain performance by multiplexing c...
Kadir Korkmaz, Joachim Bruneau-Queyreix, Sonia Ben Mokthar, Laur · 2022-02-07 · via cs.DC updates on arXiv.org

Most of today's online services (e.g., social networks, search engines, market places) are centralized, which is recognized as unsatisfactory by a majority of users for various reasons (e.g., centralized governance, censorship, loss of control over personal data). Blockchain technologies promise a new Web revolution (Web 3.0) through the decentralization of online services. However, one of the key limitations for this revolution to happen at a planetary scale is the poor performance of today's blockchains. We propose in this paper ALDER, a solution for unlocking the performance of off-the-shelf leader-based blockchains by multiplexing their consensus protocol. Our solution leverages the existence of multiple potential leaders to alleviate the bottleneck that exists at different levels of consensus protocols. To illustrate the benefits it brings to Blockchain performance, we apply ALDER to three representative blockchains, namely Algorand (Proof-of-Stake), RapidChain (Sharding-based) and Bitcoin (Proof-of-Work). Our evaluation, involving up to 10,000 nodes deployed on 100 physical machines, shows that using ALDER can provide up to a 300% improvement in both throughput and latency reduction.