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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
Cyber Physical Systems (CPS) Surveillance Using An Epidem...
Bagula Antoine, Tuyishimire Emmanuel, Ajayi Olasupo · 2019-12-17 · via cs.DC updates on arXiv.org

Vast investments have recently been made worldwide in developing the Cyber-Physical System (CPS) technology with the expectations of improving economical and societal structures. However, great care must be paid to the CPS' complexity, the impact of emerging IoT (Internet of Things) protocols on the CPS infrastructure as well as the impact of information dissemination by these protocols on the safety of these infrastructures. This paper addresses the issue of CPS safety by proposing and evaluating the performance of a CPS management framework and the analysis of the dynamics of the underlining IoT network in the cyber-space. The main contributions of this paper are in threefold. Firstly, a new CPS framework is proposed; that: 1) builds around a layered architecture to compartmentalise the CPS functionalities into different modules for efficiency and scalability and 2) uses an inner feedback loop for the efficient management of CPS infrastructure. Secondly, building upon this framework, a novel diffusion model that uses the epidemic (interference) sets to produce accurate diffusion patterns across the CPS IoT subsystem is proposed. Finally, the proposed diffusion model is numerically analysed to show how it can be used to achieve efficient CPS surveillance in order to trigger reconfiguration to re-optimise the CPS when it is under stress. in IoT settings. The numerical analysis of the diffusion model shows that interference propagates in pairwise disjoint sets, with IoT nodes migrating from "susceptible" to "attacked" statuses and finally reaching the "removed" state at a predictable time. Deployment considerations on some of the current social and public networks are also onsidered