So sánh phương pháp

	Học Liên kết Mạnh mẽ	Học Liên kết Bán Giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2017	2020
Người khởi xướng	Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.	Jeong, W. et al. / multiple independent groups
Loại	Distributed learning with Byzantine-tolerant aggregation	Distributed semi-supervised learning framework
Công trình gốc	Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017). Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. Advances in Neural Information Processing Systems, 30.	Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021).
Tên gọi khác	Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning
Liên quan	6	6
Tóm tắt	Robust Federated Learning extends standard federated learning with Byzantine-tolerant aggregation rules that protect the global model against malicious, corrupted, or unreliable clients. Instead of naively averaging client gradients, robust aggregation methods such as coordinate-wise median or Krum filter out harmful updates so that a minority of adversarial participants cannot derail training.	Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data.