FederatedScope联邦学习平台
FederatedScope是一个综合性的联邦学习平台，为学术界和工业界的各种联邦学习任务提供便捷的使用和灵活的定制。FederatedScope 基于事件驱动的架构，集成了丰富的功能集合以满足联邦学习不断增长的需求，旨在构建一个易于使用的平台，以安全有效地促进学习。
特点
- 便于使用：允许用户将自己的组件，包括数据集、模型等集成到FederatedScope中，针对特定的应用进行联邦学习。
- 事件驱动：联邦学习算法通过为参与者定义事件和相应的处理程序来模块化和表达。
代码结构：
FederatedScope
├── federatedscope
│ ├── core
│ | ├── workers # Behaviors of participants (i.e., server and clients)
│ | ├── trainers # Details of local training
│ | ├── aggregators # Details of federated aggregation
│ | ├── configs # Customizable configurations
│ | ├── monitors # The monitor module for logging and demonstrating
│ | ├── communication.py # Implementation of communication among participants
│ | ├── fed_runner.py # The runner for building and running an FL course
│ | ├── ... ..
│ ├── cv # Federated learning in CV
│ ├── nlp # Federated learning in NLP
│ ├── gfl # Graph federated learning
│ ├── autotune # Auto-tunning for federated learning
│ ├── vertical_fl # Vartical federated learning
│ ├── contrib
│ ├── main.py
│ ├── ... ...
├── scripts # Scripts for reproducing existing algorithms
├── benchmark # We release several benchmarks for convenient and fair comparisons
├── doc # For automatic documentation
├── enviornment # Installation requirements and provided docker files
├── materials # Materials of related topics (e.g., paper lists)
│ ├── notebook
│ ├── paper_list
│ ├── tutorial
│ ├── ... ...
├── tests # Unittest modules for continuous integration
├── LICENSE
└── setup.py
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