So sánh phương pháp

	Học Liên kết Mạnh mẽ	Học liên kết
Lĩnh vực	Học máy	Quyền riêng tư
Họ	Machine learning	Machine learning
Năm ra đời	2017	2017
Người khởi xướng	Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.	McMahan et al.
Loại	Distributed learning with Byzantine-tolerant aggregation	Distributed privacy-preserving machine 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.	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
Tên gọi khác	Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Liên quan	6	3
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.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.