A curated list of awesome edge computing, including Frameworks, Simulators, Tools, etc.
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Updated
Jun 17, 2026
A curated list of awesome edge computing, including Frameworks, Simulators, Tools, etc.
云边协同- collaborative inference 📚Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
云边协同- collaborative inference📚工作汇总 📚 Collaborative Inference Work Summary
About DNN compression and acceleration on Edge Devices.
云边协同- collaborative inference📚Dynamic adaptive DNN surgery for inference acceleration on the edge
北京交通大学计算机科学与技术学院系统与网络实验室
Code for paper "Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters"
A unified ecosystem and workspace for large-scale federated learning, bridging the gap between centralized model evolution and decentralized edge intelligence.
Simulation Codes for “Relay-Assisted Cooperative Federated Learning”
subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment
An auto scheduling system for stream data processing on distributed cloud-edge platforms
Code for paper "Classification-based Dynamic Network for Efficient Super-Resolution"
DisCEdge: Distributed Context Management for LLMs at the Edge
Code for paper "Dynamic Deep Neural Network Inference via Adaptive Channel Skipping"
Make an edge intelligence node based on STM32
FL-enabled architecture for scalable and proactive network slice impact classification in Intelligent Transportation Systems (ITS).
A lightweight quantization module for PyTorch models.
HERMES (Heterogeneous Application-Enabled Routing Middleware for Edge-IoT Systems) is a networking library for ESP8266, ESP32, and Raspberry Pi devices that enables self-organising multi-hop Wi-Fi networks. It combines routing with a middleware layer that allows applications to influence network behavior through custom metrics and policies.
Distributed Edge Intelligence Enabling Framework for IoT-based SHM
An end-to-end TinyML asset monitoring pipeline that trains an ANN on vibrational telemetry data and applies post-training INT8 quantization for ultra-low-power microcontroller deployments.
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