fedzero
Federated Learning Optimizer
An implementation of federated learning optimized for training on renewable energy sources and spare compute capacity to minimize carbon emissions.
Implementation and evaluation of "FedZero: Leveraging Renewable Excess Energy in Federated Learning"
19 stars
9 watching
1 forks
Language: Python
last commit: 6 months ago carbon-awareclient-selectionfederated-learninggreen-ai
Related projects:
Repository | Description | Stars |
---|---|---|
| A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. | 14 |
| An optimization framework designed to address heterogeneity in federated learning across distributed networks | 655 |
| An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
| An algorithm for distributed learning with flexible model customization during training and testing | 40 |
| Improving generalization in federated learning by seeking flat minima through optimization techniques | 82 |
| An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 |
| An implementation of a federated averaging algorithm with an extrapolation approach to speed up distributed machine learning training on client-held data. | 9 |
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| A modular JAX implementation of federated learning via posterior averaging for decentralized optimization | 50 |
| An implementation of a federated optimization algorithm for distributed machine learning | 6 |
| An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |
| An algorithm that optimizes collaboration in federated learning by clustering clients into non-overlapping coalitions based on data quantity and pairwise distribution distances. | 16 |
| An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
| An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 |
| An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 |