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

博客园 - 叶小钗
Last Week in AI
Last Week in AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
雷峰网
雷峰网
GbyAI
GbyAI
Hugging Face - Blog
Hugging Face - Blog
N
Netflix TechBlog - Medium
博客园 - 聂微东
Y
Y Combinator Blog
罗磊的独立博客
博客园_首页
小众软件
小众软件
有赞技术团队
有赞技术团队
爱范儿
爱范儿
F
Fortinet All Blogs
C
Check Point Blog
Google DeepMind News
Google DeepMind News
云风的 BLOG
云风的 BLOG
Apple Machine Learning Research
Apple Machine Learning Research
M
MIT News - Artificial intelligence
月光博客
月光博客
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 司徒正美
aimingoo的专栏
aimingoo的专栏

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
SoK: Consensus for Fair Message Ordering
[Submitted on 15 Nov 2024 (v1), last revised 24 Aug 2026 (this v · 2024-11-15 · via cs.DC updates on arXiv.org

View PDF HTML (experimental)

Abstract:Distributed ledgers rely on consensus protocols to commit messages in an agreed order. In practice, this order is often chosen for profit, which raises fairness concerns. For example, in decentralized finance, nodes exploit transaction order to extract Maximal Extractable Value (MEV). This paper systematizes the mechanisms at the consensus layer that constrain message ordering in Byzantine fault tolerant systems. We decompose adversarial behavior into three capabilities that affect ordering and analyze each ordering family against them. We organize existing work into FIFO, random, and blind ordering, and compare the metadata and assumptions each requires. We also introduce a new formalization, the $f$-Robust Median Order, which exposes a gap in existing fairness definitions based on timestamps. Finally, we describe a reusable pattern for adding fair ordering to existing BFT consensus protocols. For the protocols we analyze, fair ordering costs 1.5 to 18 times the communication of vanilla consensus, and ordering computation varies by up to five orders of magnitude across families.

Submission history

From: Zhuolun Li [view email]
[v1] Fri, 15 Nov 2024 06:33:30 UTC (503 KB)
[v2] Sat, 25 Jan 2025 14:57:05 UTC (542 KB)
[v3] Fri, 6 Jun 2025 12:09:39 UTC (1,088 KB)
[v4] Mon, 24 Aug 2026 08:02:24 UTC (555 KB)