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

C
CERT Recently Published Vulnerability Notes
U
Unit 42
T
The Blog of Author Tim Ferriss
H
Hackread – Cybersecurity News, Data Breaches, AI and More
B
Blog RSS Feed
Microsoft Azure Blog
Microsoft Azure Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
S
Securelist
L
Lohrmann on Cybersecurity
Blog — PlanetScale
Blog — PlanetScale
Recorded Future
Recorded Future
D
DataBreaches.Net
Spread Privacy
Spread Privacy
T
Threat Research - Cisco Blogs
I
Intezer
P
Palo Alto Networks Blog
Simon Willison's Weblog
Simon Willison's Weblog
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
I
InfoQ
宝玉的分享
宝玉的分享
Security Latest
Security Latest
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Threatpost
Cisco Talos Blog
Cisco Talos Blog
P
Proofpoint News Feed
博
博客园 - 司徒正美
H
Hacker News: Front Page
Y
Y Combinator Blog
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
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
NISL@THU
NISL@THU
月光博客
月光博客
有赞技术团队
有赞技术团队
Cloudbric
Cloudbric
酷 壳 – CoolShell
酷 壳 – CoolShell
G
Google Developers Blog
A
Arctic Wolf
博
博客园 - 【当耐特】
W
WeLiveSecurity
V
Visual Studio Blog
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
V
V2EX
C
Cyber Attacks, Cyber Crime and Cyber Security
S
SegmentFault 最新的问题
The GitHub Blog
The GitHub Blog
The Cloudflare Blog
Stack Overflow Blog
Stack Overflow Blog

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 Preserving Clusters in Error-Bounded Lossy Compression of Particle Data Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM UCCL-Zip: Lossless Compression Supercharged GPU Communication Training Time Prediction for Mixed Precision-based Distributed Training Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling Optimizing Stochastic Gradient Push under Broadcast Communications Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving AgileLog: A Forkable Shared Log for Agents on Data Streams Secure and Privacy-Preserving Vertical Federated Learning Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel CUTEv2: Unified and Configurable Matrix Extension for Diverse CPU Architectures with Minimal Design Overhead Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge Rebooting Microreboot: Architectural Support for Safe, Parallel Recovery in Microservice Systems A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs SMART: When is it Actually Worth Expanding a Speculative Tree? ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving OpenCLAW-P2P v7.0-P2PCLAW: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review v7.0 -- Mathematical Corrections & Ecosystem Developments Edition DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection HearthNet: Edge Multi-Agent Orchestration for Smart Homes Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios Duration-Informed Workload Scheduler Domain-Adaptive Model Merging Across Disconnected Modes Why Smaller Is Slower? Dimensional Misalignment in Compressed LLMs veScale-FSDP: Flexible and High-Performance FSDP at Scale AEG: A Baremetal Framework for AI Acceleration via Direct Hardware Access in Heterogeneous Accelerators ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving Emergent Social Structures in Autonomous AI Agent Networks: A Metadata Analysis of 626 Agents on the Pilot Protocol SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding Para-B&B: Load-Balanced Deterministic Parallelization of Solving MIP Rashomon Sets and Model Multiplicity in Federated Learning Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems NPU Design for Diffusion Language Model Inference PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving SHARe-KAN: Post-Training Vector Quantization for Cache-Resident KAN Inference Spira: Exploiting Voxel Data Structural Properties for Efficient Sparse Convolution in Point Cloud Networks Power to the Clients: Federated Learning in a Dictatorship Setting From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill Speculative Actions: A Lossless Framework for Faster Agentic Systems InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta, Haibin Lin · 2019-11-21 · via cs.DC updates on arXiv.org

When scaling distributed training, the communication overhead is often the bottleneck. In this paper, we propose a novel SGD variant with reduced communication and adaptive learning rates. We prove the convergence of the proposed algorithm for smooth but non-convex problems. Empirical results show that the proposed algorithm significantly reduces the communication overhead, which, in turn, reduces the training time by up to 30% for the 1B word dataset.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。