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A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.
Quasi-Newton Updating for Large-Scale Distributed Learning
This considerably reduces the computation and communication complexity of the proposed method.
Network Gradient Descent Algorithm for Decentralized Federated Learning
The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is well balanced, even if the data are distributed heterogeneously.
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