So sánh phương pháp

	Học liên kết	Chưng cất tri thức
Lĩnh vực	Quyền riêng tư	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2017	2015
Người khởi xướng	McMahan et al.	Hinton, G., Vinyals, O. & Dean, J.
Loại	Distributed privacy-preserving machine learning	Neural network compression (teacher–student)
Công trình gốc	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.	Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.
Tên gọi khác	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation
Liên quan	3	5
Tóm tắt	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.	Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster.