Federated Learning (FL) [1,2,3] is a learning paradigm for collaboratively learning models from isolated data without directly sharing privacy information, which helps to satisfy the requirements of privacy protection of the public. FederatedScope (FS), a comprehensive federated learning platform with event-driven architecture, aims to provide easy-to-use and flexible support for users who want to get started and customize task-specific FL procedures quickly.
For advanced users or developer
Application with FS
- Core Module References
- Federated Computer Vision Module References
- Federated Natural Language Processing Module References
- Federated Graph Learning Module References
- Auto-tuning Module References
- Attack Module References
- Federated Matrix Factorization Module References
 McMahan B, Moore E, Ramage D, et al. “Communication-efficient learning of deep networks from decentralized data”. Artificial intelligence and statistics. PMLR, 2017: 1273-1282.
 Konečný J, McMahan H B, Ramage D, et al. “Federated optimization: Distributed machine learning for on-device intelligence”. arXiv preprint arXiv:1610.02527, 2016.
 Yang Q, Liu Y, Cheng Y, et al. “Federated learning”. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2019, 13(3): 1-207.