FedWeIT
by wyjeong
This is an official Tensorflow-2 implementation of Federated Continual Learning with Inter-Client Weighted Transfer
AI summary
Federated learning framework
An implementation of Federated Continual Learning with Weighted Inter-client Transfer using TensorFlow 2.
- stars
- 102
- forks
- 27
- watching
- 2
Similar projects
Found by comparing what the projects do, not just their names.
Federated learning algorithm
A project implementing Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Federated Learning Framework
A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning
Federated Learning Framework
An implementation of a robust federated learning framework for handling noisy and heterogeneous clients in machine learning.
Federated Learning Framework
A framework for federated learning that leverages the neural tangent kernel to address statistical heterogeneity in distributed machine learning.
Federated Learning Framework
An implementation of a federated learning framework for handling data heterogeneity in decentralized settings
Federated Learning Framework
Develops an alignment framework for federated learning with non-identical client class sets
Federated Learning Framework
Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models.
Federated Learning Framework
An open source federated learning framework designed to be secure, scalable and easy-to-use for enterprise environments
Federated Learning framework
An implementation of heterogeneous federated learning with parallel edge and server computation
Federated Learning Framework
A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities.
Federated Learning Framework
A framework for collaborative distributed machine learning in enterprise environments.
Federated Learning framework
An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources.
Federated Learning Framework
Develops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks.
Data Fusion Tool
This project enables federated learning across partially class-disjoint data with curated bilateral curation.
Federated learning framework
Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.