So sánh phương pháp

	Học Liên kết Mạnh mẽ	Học Liên kết Trực tuyến
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2017	2019–2021
Người khởi xướng	Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021
Loại	Distributed learning with Byzantine-tolerant aggregation	Distributed sequential learning
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.	Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).
Tên gọi khác	Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning	OFL, federated online learning, streaming federated learning, real-time federated learning
Liên quan	6	5
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.	Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.