Tejalsjsu / DeepGradientCompressionLinks
It is implementation of Research paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING". Deep gradient compression is a technique by which the gradients are compressed before they are being sent. This approach greatly reduces the communication bandwidth and thus improves multi node training.
☆18Updated 6 years ago
Alternatives and similar repositories for DeepGradientCompression
Users that are interested in DeepGradientCompression are comparing it to the libraries listed below
Sorting:
- Partial implementation of paper "DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING"☆31Updated 4 years ago
- vector quantization for stochastic gradient descent.☆35Updated 5 years ago
- [ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training☆225Updated last year
- ☆33Updated 5 years ago
- Understanding Top-k Sparsification in Distributed Deep Learning☆24Updated 5 years ago
- Atomo: Communication-efficient Learning via Atomic Sparsification☆27Updated 6 years ago
- CMFL: Mitigating Communication Overhead for Federated Learning / PyTorch reimplementation.☆29Updated 6 years ago
- Federated Dynamic Sparse Training☆32Updated 3 years ago
- gTop-k S-SGD: A Communication-Efficient Distributed Synchronous SGD Algorithm for Deep Learning☆36Updated 6 years ago
- This is the code repository for the following paper: "Model pruning enables efficient federated learning on edge devices".☆94Updated 3 years ago
- LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning (2021 IEEE/ACM Symposium on Edge Co…☆41Updated 2 years ago
- Official Pytorch implementation of "Communication-Efficient Federated Learning with Compensated Overlap-FedAvg"☆22Updated 4 years ago
- Communication-efficient decentralized SGD (Pytorch)☆25Updated 5 years ago
- SCAFFOLD and FedAvg implementation☆61Updated 4 years ago
- ☆22Updated 4 years ago
- ☆96Updated 4 years ago
- ☆30Updated 5 years ago
- [ICML 2022] "DisPFL: Towards Communication-Efficient Personalized Federated learning via Decentralized Sparse Training"☆82Updated 3 years ago
- Algorithm: Decentralized Parallel Stochastic Gradient Descent☆46Updated 7 years ago
- [ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time☆48Updated 2 years ago
- Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity☆43Updated 4 years ago
- Adaptive Resource-Aware Split-Learning, a framework for efficient model training in IoT systems☆13Updated 2 years ago
- This repository implements FEDL using pytorch☆56Updated 4 years ago
- ☆26Updated 5 years ago
- Every work on Federated Learning Pruning☆21Updated 2 years ago
- A simulation framework for Federated Learning written in PyTorch☆208Updated 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☆174Updated 2 years ago
- ☆47Updated 3 years ago
- Federated Multi-Task Learning☆132Updated 6 years ago
- A incomplete survey for Split Learning (comprehensive enough) and Federated Learning (only most representative works)☆17Updated 3 years ago