Vertaile menetelmiä

	Hajautettu oppiminen	Stokastinen gradienttimenetelmä (SGD)
Tieteenala	Yksityisyydensuoja	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017	1951
Kehittäjä	McMahan et al.	Robbins, H. & Monro, S.
Tyyppi	Distributed privacy-preserving machine learning	First-order iterative optimization algorithm
Alkuperäislähde	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.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
Rinnakkaisnimet	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Liittyvät	3	3
Tiivistelmä	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.	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.