federated
Decentralized analysis
A collection of research projects exploring decentralized machine learning and analytics techniques
A collection of Google research projects related to Federated Learning and Federated Analytics.
695 stars
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Language: Python
last commit: 7 months ago Related projects:
Repository | Description | Stars |
---|---|---|
| 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 federated learning with prototype-based methods across heterogeneous clients | 134 |
| This project enables federated learning across partially class-disjoint data with curated bilateral curation. | 11 |
| An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
| Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
| A federated learning framework with discrepancy-aware collaboration for decentralized data training | 68 |
| 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 |
| Evaluates various methods for federated learning on different models and tasks. | 19 |
| A framework for developing and testing decentralized machine learning algorithms | 2 |
| Personalized Subgraph Federated Learning framework for distributed machine learning | 45 |
| An open-source project exploring Federated Learning model updates and their rank structure using data from various datasets. | 14 |
| Analyzes Federated Learning with Arbitrary Client Participation using various optimization strategies and datasets. | 4 |
| An unsupervised federated learning algorithm that uses cross knowledge distillation to learn meaningful data representations from local and global levels. | 69 |
| A library that provides an easy-to-use framework for simulating federated learning algorithms | 254 |