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

雷峰网
雷峰网
L
LangChain Blog
GbyAI
GbyAI
F
Fortinet All Blogs
腾讯CDC
Last Week in AI
Last Week in AI
A
About on SuperTechFans
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky
B
Blog
D
Docker
G
Google Developers Blog
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
S
SegmentFault 最新的问题
Apple Machine Learning Research
Apple Machine Learning Research
酷 壳 – CoolShell
酷 壳 – CoolShell
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
宝玉的分享
宝玉的分享
U
Unit 42
Blog — PlanetScale
Blog — PlanetScale
B
Blog RSS Feed

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
Predictions Can Only Help! Communication Efficient Byzant...
[Submitted on 13 May 2026 (v1), last revised 12 Aug 2026 (this v · 2026-05-13 · via cs.DC updates on arXiv.org

View PDF

Abstract:In Byzantine agreement with predictions each process begins with an input value and some (unreliable) prediction bits. Recently, it has been shown that with \emph{classification predictions}---where the predictions predict each process to be honest or faulty---Byzantine agreement can be completed more quickly than without predictions, circumventing the traditional $\Omega(f)$ round lower bound. However, existing algorithms either handle limited prediction errors or send too many messages. Moreover, they all exchange $\Omega(n^3)$ bits---enough to allow the processes to approximately agree on the classifications. In fact, it almost seemed necessary to share a significant number of prediction bits if one wanted to tolerate a high number of incorrect predictions.
In this paper, we show that this high level of communication is not inherent to a round-efficient protocol with predictions. We provide an unauthenticated algorithm with near-optimal $\tilde{\mathcal{O}}(n^2)$ communication complexity and optimal resilience $t < n/3$. Furthermore, with authentication, we give an algorithm with optimal $\mathcal{O}(n^2\kappa)$ communication complexity (where $\kappa$ is a security parameter) and near-optimal resilience $t < (\frac{1}{2} - \epsilon)n$ for any constant $\epsilon > 0$. All of our results have optimal round complexity for any number of errors in the predictions.

Submission history

From: Muhammad Ayaz Dzulfikar [view email]
[v1] Wed, 13 May 2026 03:14:51 UTC (40 KB)
[v2] Wed, 12 Aug 2026 07:07:48 UTC (57 KB)