Collaborative Edge
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Related Articles from SNS
CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
Announce Type: new Abstract: Recently, mobile edge computing (MEC)-enabled collaborative deep neural network (DNN) inference has emerged as a promising approach for delivering intelligent services to resource-constrained mobile devices. A representative scenario is multi-user collaborative edge inference, where distinct devices independently partition their DNN models and offload backend computation to a common edge server over wireless networks. However, determining the optimal DNN...
Collaborative Edge-to-Server Inference for Vision-Language Models
arXiv:2512.16349v2 Announce Type: replace Abstract: We propose a collaborative edge-to-server inference framework for vision-language models (VLMs) that reduces communication cost while maintaining inference accuracy. In typical deployments, visual data captured at edge devices (clients) is transmitted to the server for VLM inference. However, transmitting full-resolution images incurs high communication cost.
CREWS: Collaborative Robust Edge WiFi Sensing with Asynchronous and Incomplete Observations
arXiv:2605.30356v1 Announce Type: new Abstract: Existing collaborative WiFi sensing systems rely on perfect node synchronization and complete data availability. However, real-world edge deployments suffer from heterogeneous computing and network dropouts, leading to asynchronous and incomplete features. We propose CREWS, a robust collaborative sensing framework that inherently resists these network volatility.
AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments
arXiv:2606.08197v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly propelled the advancement of edge intelligence and have been widely deployed across various scenarios, including autonomous driving, industrial inspection, and personalized IoT services. However, the collaborative adaptation of LLMs on edge devices continues to face formidable challenges due to strict data privacy constraints, highly heterogeneous computing and communication resources, and the...
EES-CND: Collaborative Neural Decision-Making for Drift-Aware Fault-Tolerant Edge-Cloud Service Placement
arXiv:2606.02259v1 Announce Type: new Abstract: The edge-cloud paradigm improves service delivery by orchestrating resources across edge nodes and cloud data centres. These environments consist of heterogeneous, interconnected computing nodes that cooperate to deliver continuous services. However, their scale and complexity increase vulnerability to failures from hardware malfunctions, software defects, and dynamic operating conditions.
DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
arXiv:2605.31427v1 Announce Type: new Abstract: Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retraining and are not well-suited for collaborative settings with partitioned data. In realistic graph systems, cross-partition edges are unavoidable, but direct sharing of graph structure between clients may violate privacy constraints. We propose DG-CoLearn, a...
Tiny Collaborative Inference for Occlusion-Robust Object Detection
Announce Type: new Abstract: Small edge devices such as IoT surveillance nodes and search-and-rescue (SAR) platforms are increasingly expected to run computer vision locally. On ultra-low-end hardware, however, object detection is limited by available memory and compute, by communication costs when several devices cooperate, and by the loss of accuracy caused by occlusion. The work evaluates occlusion-robust object detection on devices with less than 1 MB SRAM by combining an MCUNet...
Agentic AI-Enhanced Semantic Communications: Foundations, Architecture, and Applications
Announce Type: replace Abstract: Semantic communications (SemCom), as one of the key technologies for 6G, is shifting networks from bit transmission to semantic information exchange. On this basis, introducing agentic artificial intelligence (AI) with perception, memory, reasoning, and action capabilities provides a practicable path to intelligent communications. This paper provides a systematic exposition of how agentic AI empowers SemCom from the perspectives of research foundations,...
CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation
arXiv:2605.30668v1 Announce Type: new Abstract: Dialogue topic segmentation is critical in many human-AI collaborative applications which requires identifying heterogeneous boundary cues, including lexical transitions near utterance edges and semantic discontinuities across utterances. Existing utterance models often dilute these local lexical signals. We propose CobSeg, a novel multi-branch architecture that separates coherence-level semantic continuity from lexical boundary transitions and...
Google CEO called out 'biggest AI budget problem' of companies world over from IO stage with a solution
Google CEO Sundar Pichai shifted the AI conversation from to economics at this years’s Google I/O conference. Pichai warned that the companies around the world are blowing through their annual AI budgets by May due to runaway token usage. Pichai said the rapid rise of AI agents has created unprecedented costs for enterprises.