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

Y
Y Combinator Blog
腾讯CDC
Recent Announcements
Recent Announcements
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Hugging Face - Blog
Hugging Face - Blog
H
Help Net Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Last Week in AI
Last Week in AI
博客园_首页
D
DataBreaches.Net
P
Proofpoint News Feed
云风的 BLOG
云风的 BLOG
V
Visual Studio Blog
月光博客
月光博客
Jina AI
Jina AI
Stack Overflow Blog
Stack Overflow Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 【当耐特】
Vercel News
Vercel News
WordPress大学
WordPress大学
J
Java Code Geeks
博客园 - 聂微东
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42

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
OpenFLAME: A Federated Spatial Naming Infrastructure
Sagar Bharadwaj, Ziyong Ma, Ivan Liang, Michael Farb, Anthony Ro · 2024-11-07 · via cs.DC updates on arXiv.org

Spatial applications, i.e., applications that tie digital information with the physical world, have improved many of our daily activities, such as navigation and ride-sharing. This class of applications also holds significant promise of enabling new industries such as augmented reality and robotics. The development of these applications is enabled by a system that can resolve real-world locations to names, or a spatial naming system. Today, mapping platforms provided by organizations like Google and Apple serve as spatial naming systems. These maps are centralized and primarily cover outdoor spaces. We envision that future spatial applications, such as persistent world-scale augmented reality, would require detailed and precise spatial data across indoor and outdoor spaces. The scale of cartography efforts required to survey indoor spaces and their privacy needs inhibit existing centralized maps from incorporating such spaces into their platform. In this paper, we present the design and implementation of OpenFLAME stands for Open Federated Localization and Mapping Engine, a federated spatial naming system, or in other words, a federated mapping infrastructure. It enables independent parties to manage and serve their own maps of physical regions. This unlocks scalability of map management, isolation, and privacy of maps. The discovery system that identifies maps hosted at a given location is a primary component of our system. We implement OpenFLAME on top of the existing Domain Name System (DNS), which enables us to leverage its existing infrastructure. We implement map services such as address-to-location mapping, routing, and localization on top of our federated mapping infrastructure.