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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
Composable Industrial Internet Applications for Tiered Ar...
K. Eric Harper, Thijmen de Gooijer, Karen Smiley · 2016-02-17 · via cs.DC updates on arXiv.org

A single vendor cannot provide complete IIoT end-to-end solutions because cooperation is required from multiple parties. Interoperability is a key architectural quality. Composability of capabilities, information and configuration is the prerequisite for interoperability, supported by a data storage infrastructure and defined set of interfaces to build applications. Secure collection, transport and storage of data and algorithms are expectations for collaborative participation in any IIoT solution. Participants require control of their data ownership and confidentiality. We propose an Internet of Things, Services and People (IoTSP) application development and management framework which includes components for data storage, algorithm design and packaging, and computation execution. Applications use clusters of platform services, organized in tiers, and local access to data to reduce complexity and enhance reliable data exchange. Since communication is less reliable across tiers, data is synchronized between storage replicas when communication is available. The platform services provide a common ecosystem to exchange data uniting data storage, applications, and components that process the data. Configuration and orchestration of the tiers are managed using shared tools and facilities. The platform promotes the data storage components to be peers of the applications where each data owner is in control of when and how much information is shared with a service provider. The service components and applications are securely integrated using local event and data exchange communication channels. This tiered architecture reduces the cyber attack surface and enables individual tiers to operate autonomously, while addressing interoperability concerns. We present our framework using predictive maintenance as an example, and evaluate compatibility of our vision with an emerging set of standards.