AdamWei-boop / Federated-Learning-with-Model-QuantizationLinks
Federated learning with model quantization
☆16Updated 3 years ago
Alternatives and similar repositories for Federated-Learning-with-Model-Quantization
Users that are interested in Federated-Learning-with-Model-Quantization are comparing it to the libraries listed below
Sorting:
- This is the code repository for the following paper: "Model pruning enables efficient federated learning on edge devices".☆92Updated 3 years ago
- An implementation of FedPAQ using different experimental parameters. We will be looking at different variations of how, r(number of clien…☆22Updated 4 years ago
- [ACM MobiCom 2022] "PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning" by Che…☆72Updated 3 years ago
- Codebase for "Greedy Shapley Client Selection for Communication-Efficient Federated Learning"☆18Updated last year
- [ICML 2022] "DisPFL: Towards Communication-Efficient Personalized Federated learning via Decentralized Sparse Training"☆82Updated 3 years ago
- diaoenmao / HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients☆172Updated 2 years ago
- Releasing the source code Version1.☆170Updated 4 years ago
- PyTorch implementation of Joint Privacy Enhancement and Quantization in Federated Learning (IEEE TSP 2023, IEEE ICASSP 2023, IEEE ISIT 20…☆18Updated last year
- ☆32Updated 3 years ago
- This repository implements FEDL using pytorch☆55Updated 4 years ago
- Official implementations for "Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID D…☆22Updated last year
- LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning (2021 IEEE/ACM Symposium on Edge Co…☆41Updated 2 years ago
- Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation☆20Updated last year
- [NeurIPS 2022] "FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction" by Samiul Alam, Luyang Liu, Ming Yan,…☆64Updated last year
- Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.☆46Updated 2 years ago
- FlexCFL: A clustered federated learning framework based on TF2.0. Support frameworks: FlexCFL, FedGroup, FedAvg, IFCA, FeSEM, et al.☆49Updated 2 years ago
- This is a platform containing the datasets and federated learning algorithms in IoT environments.☆67Updated 9 months ago
- Codes for the paper FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning☆46Updated 2 years ago
- SPATL: Salient Prameter Aggregation and Transfer Learning for Heterogeneous Federated Learning☆23Updated 2 years ago
- Federated Dynamic Sparse Training☆31Updated 3 years ago
- The open-souce code of FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data, accepted …☆19Updated last year
- CMFL: Mitigating Communication Overhead for Federated Learning / PyTorch reimplementation.☆29Updated 5 years ago
- Study of data imbalance and asynchronous aggregation algorithm on Federated Learning system (using PySyft)☆62Updated last year
- FedDCT: A Novel Federated Learning Approach for Training Large Convolutional Neural Networks☆40Updated 2 years ago
- Diverse Client Selection for Federated Learning via Submodular Maximization☆32Updated 3 years ago
- Repo for MobiSys 2021 paper: "ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition".☆39Updated 2 years ago
- Active Client Selection for Federated Learning☆49Updated 2 years ago
- Codebase for An Efficient Framework for Clustered Federated Learning.☆116Updated 4 years ago
- Investigating Split Learning and Federate Learning☆85Updated 5 years ago
- SRDS 2020: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things☆112Updated 4 years ago