Stars
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
The python code for paper "Multi-objective Deep Reinforcement Learning for Mobile Edge Computing"
Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM
[IJCAI'24 - FlagVNE] Implementation of our paper "FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation", accepted by IJCAI 2024.
[TSC'23 - HRL-ACRA] Implementation of our paper "Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning", accepted by IEEE Transac…
Some classical edge server placement algorithms are implemented.
This is a paper list about Resource Allocation in Network Functions Virtualization (NFV) and Software-Defined Networking (SDN).
[ICC'21 - DRL-SFCP] Implementation of our paper "DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning", accepted by ICC 2021.
NFVdeep: Deep Reinforcement Learning for Online Orchestration of Service Function Chains
NFV simulation experiment to deploy service function chains
Dynamic Enhanced Resource-Aware Load Balancing Algorithm for Cloud Computing
Awesome LLMs on Device: A Comprehensive Survey
Framework that supports pipeline federated split learning with multiple hops.
Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.
Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"
A collection of pre-trained, state-of-the-art models in the ONNX format
This repository contains the program used to train and evaluate a Branched DNN capable of early-exit semantic segmentation, suited for an edge-cloud co-inference scenario in smart cities..
Code for paper "Joint Adaptive Resolution Selection and Conditional Early Exiting for Efficient Video Recognition on Edge Devices"
The algorithm was implemented in Python, making use of its powerful libraries for machine learning and data processing. The used codes for the implementation of the batch with the models are found …
This repository presents the source code for the paper titled "Reinforcement Learning based Collaborative DNN Inference for Edge Intelligence"."
Code for paper "TLEE: Temporal-wise and Layer-wise Early Exiting Network for Efficient Video Recognition on Edge Devices"
Official Implementation of "AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing"
Implementation of the algorithm described in "Fully Distributed Deep Learning Inference on Resource-Constrained Edge Devices"
Code for paper "Joint Architecture Design and Workload Partitioning for DNN Inference on Industrial IoT Clusters"
Code for ACM MobiCom 2024 paper "FlexNN: Efficient and Adaptive DNN Inference on Memory-Constrained Edge Devices"
Code for paper "Locally Distributed Deep Learning Inference on Edge Device Clusters"