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
- Code Implemntion from the article Multi-Armed Bandit Based Client Schedulingfor Federated Learning☆16Updated 4 years ago
- Code for Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence☆15Updated 4 years ago
- ☆35Updated 2 years ago
- Official code for "Federated Learning under Heterogeneous and Correlated Client Availability" (INFOCOM'23)☆28Updated 2 years ago
- Multiple Edge Servers Assignment for Local Device in Hierarchical Federated Learning☆18Updated 3 years ago
- Official implementations for "Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID D…☆20Updated last year
- This repository implements FEDL using pytorch☆53Updated 4 years ago
- SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks☆13Updated 2 years ago
- Migration of Edge-based Distributed Federated Learning☆25Updated 2 years ago
- Federated Learning over Wireless Networks☆45Updated 3 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
- ☆11Updated 4 years ago
- Adaptive Offloading of Federated Learning on IoT Devices☆72Updated 2 years ago
- Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation☆34Updated 3 years ago
- We will implement this framework.☆30Updated 2 years ago
- ☆10Updated last year
- Federated Reinforcement Learning project☆27Updated 2 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☆36Updated 11 months ago
- Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang and Zhihua Zhang. Federated Reinforcement Learning with Environment Heterogeneity. AISTATS, …☆58Updated 3 years ago
- Code of "HSFL: Efficient and Privacy-Preserving Offloading for Split and Federated Learning in IoT Services" published on International C…☆15Updated last year
- FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs☆19Updated 2 years ago
- inherit from https://github.com/iqua/flsim☆9Updated 4 years ago
- Publication catalog for research on Federated RL (FRL).☆79Updated 3 years ago
- A simulation of energy consumption of a federated learning system based on the non-orthogonal multiple access (NOMA) transmission protoco…☆27Updated 4 years ago
- Federated Reinforcement Learning☆12Updated 5 years ago
- Asynchronous Federated Learning☆19Updated 3 years ago
- Code for the case study presented in "Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Sys…☆25Updated 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