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Found 5 papers, 3 papers with code
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
However, existing graph-based methods fails to consider the bias offsets of users (items).
SPAN: A Stochastic Projected Approximate Newton Method
SPAN computes the inverse of the Hessian matrix via low-rank approximation and stochastic Hessian-vector products.
Variance Reduced Local SGD with Lower Communication Complexity
To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training.
Long-term Joint Scheduling for Urban Traffic
Recently, the traffic congestion in modern cities has become a growing worry for the residents.
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup.
