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

爱范儿
爱范儿
量子位
人人都是产品经理
人人都是产品经理
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - Franky
Recent Announcements
Recent Announcements
A
About on SuperTechFans
Microsoft Security Blog
Microsoft Security Blog
N
Netflix TechBlog - Medium
H
Help Net Security
博客园 - 三生石上(FineUI控件)
博客园 - 司徒正美
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
MyScale Blog
MyScale Blog
The Cloudflare Blog
S
SegmentFault 最新的问题
H
Hackread – Cybersecurity News, Data Breaches, AI and More
J
Java Code Geeks
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
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
宝玉的分享
宝玉的分享
Vercel News
Vercel News

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
Breaking the $f+1$ Barrier: Executing Payment Transaction...
Rida A. Bazzi, Sara Tucci-Piergiovanni · 2023-01-25 · via cs.DC updates on arXiv.org

We consider the problem of supporting payment transactions in an asynchronous system in which up to $f$ validators are subject to Byzantine failures under the control of an adaptive adversary. It was shown that this problem can be solved without consensus by using byzantine quorum systems (requiring at least $2f+1$ validations per transaction in asynchronous systems). We show that it is possible to validate transactions in parallel with less than $f$ validations per transaction if each transaction spends no more that a small fraction of a balance. Our solution relies on a novel quorum system that we introduce in this paper and that we call $(k_1,k_2)$-quorum systems. In the presence of a non-adaptive adversary, these systems can be used to allow up to $k_1$ transactions to be validated concurrently and asynchronously but prevent more than $k_2$ transactions from being validated. If the adversary is adaptive, these systems can be used to allow $k_1$ transaction to be validated and prevent more than $k'_2 > k_2$ transactions from being validated, the difference $k'_2-k_2$ being dependent on the quorum system's {\em validation slack}, which we define in this paper. Using $(k_1,k_2)$-quorum systems, a payer can execute multiple partial spending transactions to spend a portion of its initial balance with less than full quorum validation (less than $f$ validations per transaction) then reclaim any remaining funds using one fully validated transaction, which we call a {\em settlement} transaction.