FEDVSSL
Video learning framework
Implementation of Federated Self-Superivised Learning for video understanding
This is the official impelementation of "FVSSL Algorithm"
24 stars
6 watching
2 forks
Language: Python
last commit: about 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of heterogeneous federated learning with parallel edge and server computation | 17 |
| An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
| A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 49 |
| This project develops an adaptive kernel approach to federated learning of heterogeneous causal effects. | 1 |
| Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |
| 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 |
| An implementation of a personalized federated learning framework with decentralized sparse training and peer-to-peer communication protocol. | 72 |
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| Evaluates various methods for federated learning on different models and tasks. | 19 |
| A federated learning platform for computer vision tasks using PaddleFL and PaddleDetection | 112 |
| A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 57 |
| This project enables federated learning across partially class-disjoint data with curated bilateral curation. | 11 |
| A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
| Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
| Provides a framework and theoretical foundation for Federated Reinforcement Learning with Byzantine Resilience in distributed systems | 85 |