Salīdzināt metodes

	Federatīvā apmācība	Stohastiskā gradienta metode (SGD)
Nozare	Privātums	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017	1951
Autors	McMahan et al.	Robbins, H. & Monro, S.
Tips	Distributed privacy-preserving machine learning	First-order iterative optimization algorithm
Pirmavots	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.
Citi nosaukumi	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
Saistītās	3	3
Kopsavilkums	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.