Sammenlign metoder

	Fødereret læring	Knowledge Distillation
Fagområde	Privatlivsbeskyttelse	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2017	2015
Ophavsperson	McMahan et al.	Hinton, G., Vinyals, O. & Dean, J.
Type	Distributed privacy-preserving machine learning	Neural network compression (teacher–student)
Oprindelig kilde	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.
Aliasser	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation
Relaterede	3	5
Resumé	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.