Research Programmes
Books
- 杨强、黄安埠、刘洋、陈天健 《联邦学习实战》 电子工业出版社, p. 340 (2021).
- Yang, Q., Fan, L. & Yu, H. (Eds.). (2020). Federated Learning: Privacy and Incentive. Springer International Publishing, Switzerland, p. 282. (Google Scholar)
- 杨强、刘洋、程勇、 康炎、陈天健、于涵 《联邦学习》 电子工业出版社, p. 208 (2020).
- Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T. & Yu, H. (2019). Federated Learning. Morgan & Claypool Publishers, San Rafael, CA, USA, p. 207.
Journal Special Issues
- Special Issue on Trustable, Verifiable, and Auditable Federated Learning, IEEE Transactions on Big Data (TBD), 2022.
- Special Issue on Federated Learning: Algorithms, Systems, and Applications, ACM Transactions on Intelligent Systems and Technology (TIST), 2021.
- Special Issue on Federated Machine Learning, IEEE Intelligent Systems (IS), 2019.
Workshops
- FL-IJCAI'22, Vienna, Austria
- FL-AAAI-22, Vancouver, BC, Canada (Virtual)
- FL-NeurIPS'21 (Virtual)
- The Federated Learning Workshop, 2021, Paris, France (Hybrid)
- PDFL-EMNLP'21, Bilbao, Spain (Virtual)
- FTL-IJCAI'21, Montreal, QB, Canada (Virtual)
- DeepIPR-IJCAI'21, Montreal, QB, Canada (Virtual)
- FL-ICML'21 (Virtual)
- RSEML-AAAI-21 (Virtual)
- NeurIPS-SpicyFL'20, Vancouver, BC, Canada (Virtual)
- FL-IJCAI'20, Yokohama, Japan (Virtual)
- FL-ICML'20, Vienna, Austria (Virtual)
- FL-IBM'20, New York, NY, USA
- FL-NeurIPS'19, Vancouver, BC, Canada
- FL-IJCAI'19, Macau
- FL-Google'19, Seattle, WA, USA
Conference Special Tracks
Wikipedia
Benchmark Datasets
Tutorials
Standardization Effort
Comics
Youtube Channels
Frameworks
Communities