yj4889 / yj4889-Optimized-Quantization-for-Convolutional-Deep-Neural-Networks-in-Federated-Learning
Federated learning is a distributed learning method that trains a deep network on user devices without collecting data from central server. It is useful when the central server can’t collect data. However, the absence of data on central server means that deep network compression using data is not possible. Deep network compression is very import…
☆13Updated 4 years ago
Alternatives and similar repositories for yj4889-Optimized-Quantization-for-Convolutional-Deep-Neural-Networks-in-Federated-Learning:
Users that are interested in yj4889-Optimized-Quantization-for-Convolutional-Deep-Neural-Networks-in-Federated-Learning are comparing it to the libraries listed below
- Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks☆16Updated 3 years ago
- Official code for "Federated Learning under Heterogeneous and Correlated Client Availability" (INFOCOM'23)☆28Updated 2 years ago
- Code Implemntion from the article Multi-Armed Bandit Based Client Schedulingfor Federated Learning☆16Updated 4 years ago
- SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks☆13Updated 2 years ago
- ☆11Updated 4 years ago
- Code for Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence☆15Updated 4 years ago
- Official implementations for "Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID D…☆20Updated last year
- Migration of Edge-based Distributed Federated Learning☆25Updated 2 years ago
- ☆35Updated 2 years ago
- We will implement this framework.☆30Updated 2 years ago
- FLASH-RL (Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning) is a novel and effective strategy f…☆39Updated 11 months ago
- Federated Learning over Wireless Networks☆45Updated 3 years ago
- In this work, we propose a novel formulation titled Federated Deep Q Networks (F-DQN) to perform distributed learning for Deep RL algorit…☆20Updated 4 years ago
- Multiple Edge Servers Assignment for Local Device in Hierarchical Federated Learning☆18Updated 3 years ago
- ☆11Updated last year
- Code of "HSFL: Efficient and Privacy-Preserving Offloading for Split and Federated Learning in IoT Services" published on International C…☆15Updated last year
- ☆13Updated last year
- A simulation of energy consumption of a federated learning system based on the non-orthogonal multiple access (NOMA) transmission protoco…☆27Updated 4 years ago
- The code for the paper "Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation"☆29Updated 3 years ago
- Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing☆35Updated 11 months ago
- Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation☆34Updated 3 years ago
- Edge Computing AI and Smart Contract☆10Updated last year
- Thesis in Federated Learning using an Edge/Cloud Computing architecture☆10Updated 4 years ago
- Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT☆30Updated last year
- [NeurIPS 2024] Code for Federated Ensemble-Directed Offline Reinforcement Learning☆22Updated 6 months ago
- Welcome to FLSim_V2, a PyTorch based federated Reinforcement learning simulation framework☆10Updated 2 years ago
- Adaptive Offloading of Federated Learning on IoT Devices☆72Updated 2 years ago
- This repository implements FEDL using pytorch☆53Updated 4 years ago
- [IoTDI 2023/ML4IoT 2023] Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks☆37Updated last year
- ☆16Updated 2 years ago