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

Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
C
Check Point Blog
I
InfoQ
A
About on SuperTechFans
Engineering at Meta
Engineering at Meta
月光博客
月光博客
Recent Announcements
Recent Announcements
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Tailwind CSS Blog
Y
Y Combinator Blog
博客园 - Franky
博客园_首页
罗磊的独立博客
量子位
美团技术团队
T
The Blog of Author Tim Ferriss
Last Week in AI
Last Week in AI
大猫的无限游戏
大猫的无限游戏
爱范儿
爱范儿
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
Martin Fowler
Martin Fowler
博客园 - 叶小钗
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
Threshold-based Network Structural Dynamics
Evangelos Kipouridis, Paul G. Spirakis, Kostas Tsichlas · 2021-03-09 · via cs.DC updates on arXiv.org

The interest in dynamic processes on networks is steadily rising in recent years. In this paper, we consider the $(α,β)$-Thresholded Network Dynamics ($(α,β)$-Dynamics), where $α\leq β$, in which only structural dynamics (dynamics of the network) are allowed, guided by local thresholding rules executed in each node. In particular, in each discrete round $t$, each pair of nodes $u$ and $v$ that are allowed to communicate by the scheduler, computes a value $\mathcal{E}(u,v)$ (the potential of the pair) as a function of the local structure of the network at round $t$ around the two nodes. If $\mathcal{E}(u,v) < α$ then the link (if it exists) between $u$ and $v$ is removed; if $α\leq \mathcal{E}(u,v) < β$ then an existing link among $u$ and $v$ is maintained; if $β\leq \mathcal{E}(u,v)$ then a link between $u$ and $v$ is established if not already present. The microscopic structure of $(α,β)$-Dynamics appears to be simple, so that we are able to rigorously argue about it, but still flexible, so that we are able to design meaningful microscopic local rules that give rise to interesting macroscopic behaviors. Our goals are the following: a) to investigate the properties of the $(α,β)$-Thresholded Network Dynamics and b) to show that $(α,β)$-Dynamics is expressive enough to solve complex problems on networks. Our contribution in these directions is twofold. We rigorously exhibit the claim about the expressiveness of $(α,β)$-Dynamics, both by designing a simple protocol that provably computes the $k$-core of the network as well as by showing that $(α,β)$-Dynamics is in fact Turing-Complete. Second and most important, we construct general tools for proving stabilization that work for a subclass of $(α,β)$-Dynamics and prove speed of convergence in a restricted setting.