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

F
Fortinet All Blogs
爱范儿
爱范儿
V
Visual Studio Blog
Engineering at Meta
Engineering at Meta
B
Blog
云风的 BLOG
云风的 BLOG
G
Google Developers Blog
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
U
Unit 42
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
WordPress大学
WordPress大学
D
DataBreaches.Net
B
Blog RSS Feed
小众软件
小众软件
人人都是产品经理
人人都是产品经理
I
InfoQ
P
Proofpoint News Feed
博客园 - 聂微东
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
J
Java Code Geeks
The GitHub Blog
The GitHub Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报

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
CollaChain: A BFT Collaborative Middleware for Decentrali...
Deepal Tennakoon, Yiding Hua, Vincent Gramoli · 2022-03-23 · via cs.DC updates on arXiv.org

The sharing economy is centralizing services, leading to misuses of the Internet. We can list growing damages of data hacks, global outages and even the use of data to manipulate their owners. Unfortunately, there is no decentralized web where users can interact peer-to-peer in a secure way. Blockchains incentivize participants to individually validate every transaction and impose their block to the network. As a result, the validation of smart contract requests is computationally intensive while the agreement on a unique state does not make full use of the network. In this paper, we propose Collachain, a new byzantine fault tolerant blockchain compatible with the largest ecosystem of DApps that leverages collaboration. First, the pariticipants executing smart contracts collaborate to validate the transactions, hence halving the number of validations required by modern blockchains (e.g., Ethereum, Libra). Second, the participants in the consensus collaborate to combine their block proposal into a superblock, hence improving throughput as the system grows to hundreds of nodes. In addition, Collachain offers the possibility to its users to interact securely with each other without downloading the blockchain, hence allowing interactions via mobile devices. Collachain is effective at outperforming the Concord and Quorum blockchains and its throughput peaks at 4500 TPS under a Twitter DApp (Decentralized Application) workload. Finally, we demonstrate Collachain's scalability by deploying it on 200 nodes located in 10 countries over 5 continents.