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This paper presents a comprehensive survey and taxonomy of federated learning over human-body communication for on-body edge intelligence, including a scheduling vignette called BODYFED-HBC.
This paper proposes MODIAD, a framework for multimodal online distributed industrial anomaly detection, addressing resource constraints with a Multi-class Intelligent Scheduling problem and a Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy. Experiments on MVTec 3D-AD and Eyecandies datasets demonstrate superior performance and efficiency.
This paper presents an AI-driven framework for energy-efficient environmental monitoring in smart cities using edge intelligence and TinyML, which dynamically activates sensors based on spatiotemporal conditions to reduce energy consumption and extend sensor lifespan.
FusionSense introduces a tri-stage near-sensor learning framework for multimodal edge intelligence that jointly reduces compute and communication by using fusion-aware filtering, achieving up to 33× energy savings and significant data-reduction gains on RGB-Depth/LiDAR tasks.
AutoMCU is a multi-agent system leveraging LLMs to automate neural network design for microcontroller units, significantly reducing customization time while ensuring feasibility under hardware constraints.