diffdist
Distributed Backpropagation Library
Enables backpropagation in distributed settings and facilitates model parallelism using differentiable communication between processes
62 stars
1 watching
6 forks
Language: Python
last commit: over 4 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A Haskell library providing automatic heterogeneous back-propagation for differentiable programming and deep learning applications. | 180 |
| A library that allows gradients to be propagated through sorting operations, enabling differentiable sorting networks. | 107 |
| An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 |
| This repository provides an implementation of a differentially private federated learning algorithm designed to improve the robustness and performance of federated machine learning systems. | 42 |
| A Haskell-based framework for processing and distributing large datasets across multiple nodes in parallel. | 116 |
| A PyTorch implementation of a method for improving semi-supervised learning in federated settings by adapting pseudo labels to balance classes. | 7 |
| Automatically trains models from large foundation models to perform specific tasks with minimal human intervention. | 2,022 |
| This project presents a method for federated domain generalization with adjustment, allowing multiple models to learn from each other across different domains. | 43 |
| An implementation of Personalized Federated Learning with Gaussian Processes using Python. | 32 |
| A Scala library for computing diffs and patches of JSON data | 315 |
| An implementation of federated learning with sparse training and readjustment mechanisms to reduce communication overhead while maintaining model performance. | 29 |
| A library for managing subprocesses in Python. | 1,702 |
| Training and deploying large language models on computer vision tasks using region-of-interest inputs | 517 |
| This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |
| An algorithm that breaks secure aggregation protocols in federated learning by recovering individual model updates from aggregated sums | 14 |