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

G
Google Developers Blog
Apple Machine Learning Research
Apple Machine Learning Research
小众软件
小众软件
Recent Announcements
Recent Announcements
阮一峰的网络日志
阮一峰的网络日志
IT之家
IT之家
A
About on SuperTechFans
量子位
Engineering at Meta
Engineering at Meta
B
Blog
The Cloudflare Blog
博客园 - 【当耐特】
Hugging Face - Blog
Hugging Face - Blog
Y
Y Combinator Blog
J
Java Code Geeks
D
DataBreaches.Net
aimingoo的专栏
aimingoo的专栏
T
Tailwind CSS Blog
H
Help Net Security
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
V
V2EX
Stack Overflow Blog
Stack Overflow Blog
C
Check Point Blog
酷 壳 – CoolShell
酷 壳 – CoolShell

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
Random Sampling Applied to the MST Problem in the Node Co...
Krzysztof Nowicki · 2018-07-24 · via cs.DC updates on arXiv.org

The Congested Clique model proposed by Lotker et al.[SICOMP'05] was introduced in order to provide a simple abstraction for overlay networks. Congested Clique is a model of distributed (or parallel) computing, in which there are $n$ players with unique identifiers from set [n], which perform computations in synchronous rounds. Each round consists of the phase of unlimited local computation and the communication phase. While communicating, each pair of players is allowed to exchange a single message of size $O(\log n)$ bits. Since, in a single round, each player can communicate with even $Θ(n)$ other players, the model seems to be to powerful to imitate bandwidth restriction emerging from the underlying network. In this paper we study a restricted version of the Congested Clique model, the Node Congested Clique (NCC) model, proposed by Augustine et al.[arxiv1805], in which a player is allowed to send/receive only $O(\log n)$ messages per communication phase. More precisely, we provide communication primitives that improve the round complexity of the MST algorithm by Augustine et al. [arxiv1805] to $O(\log^3 n)$ rounds, and give an $O(\log^2 n)$ round algorithm solving the Spanning Forest (SF) problem. Furthermore, we present an approach based on the random sampling technique by Karger et al.[JACM'95] that gives an $O(\log^2 n \log Δ/ \log \log n)$ round algorithm for the Minimum Spanning Forest (MSF) problem. Besides the faster SF/ MSF algorithms we consider the key contributions to be - an efficient implementation of basic protocols in the NCC model - a tighter analysis of a special case of the sampling approach by Karger et al.[JACM'95] and related results by Pemmaraju and Sardeshmukh [FSTTCS'16] - efficient k-sparse recovery data structure that requires $O((k +\log n)\log n\log k)$ bits and provides recovery procedure that requires $O((k +\log n)\log k)$ steps