PACFL
Federated learner
Implementation of federated learning algorithms for distributed machine learning on private client data
Official Code for PACFL AAAI 2023
37 stars
1 watching
13 forks
Language: Python
last commit: over 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of Fast Federated Learning under device unavailability for minimizing latency and achieving optimal convergence rates | 9 |
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 |
| Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
| An implementation of heterogeneous federated learning with parallel edge and server computation | 17 |
| An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 |
| An ICLR 2023 paper implementation in PyTorch of Federated Feature Augmentation for federated learning with data augmentation and medical image analysis. | 57 |
| 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 implementation of Fair and Consistent Federated Learning using Python. | 20 |
| This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
| An implementation of federated multi-task learning with laplacian regularization across various datasets | 16 |
| An implementation of an online federated learning algorithm with multiple kernels for personalized machine learning | 0 |
| A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 49 |
| Enables the training and validation of machine learning models on distributed datasets in a secure and scalable manner. | 274 |
| An implementation of a federated learning algorithm for handling heterogeneous data | 6 |