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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
Urban Green Governance: IoT-Driven Management and Enhance...
Antonio Salis, Gabriele Troina, Gianluca Boanelli, Marco Ottavia · 2025-07-16 · via cs.DC updates on arXiv.org

The efficient design and management of public green spaces is a key factor in promoting the health and well-being of urban population, as emphasized by the WHO, UNEP, and EEA. These areas serve as the "green lungs" of the urban ecosystem, playing a vital role in enhancing quality of life thanks to the provision of ecosystem services. In this context, the Smart Green City use case in Campobasso municipality, funded by the Italian Ministry of Enterprises (MIMIT), emerges as an innovative model for the sustainable management of green urban areas through the adoption of an advanced system of emerging technologies integrated and interoperable. The project integrates IoT systems and data-driven governance platforms, enabling real-time monitoring of the health status of trees and green areas via a Decision Support System (DSS). It also facilitates the collection and analysis of data from diverse sources, including weather conditions, air quality, soil moisture, pollution levels. The resulting cloud-based platform supports a holistic real time decision making for green urban managers, technical experts and operational staff. It enables intelligent control and management of urban green spaces using Tree Talker sensors, integrated with soil moisture and water potential monitoring systems. Thanks to predictive models based on machine learning algorithms and real time data provided by IoT sensors, irrigation of public parks can be optimized by providing suggestions on when and how much water to apply. Customized alerts layers are also activated warning users when monitored parameters, such as soil temperature, humidity, or water potential, exceed predefined thresholds. This Use Case demonstrates how digitalization, IoT sensors fusion and technological innovation can support sustainable urban governance, fostering environmental resilience and improving citizens quality of life.