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Found 5 papers, 2 papers with code
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
In this paper, we design EPISODE, the very first algorithm to solve FL problems with heterogeneous data in the nonconvex and relaxed smoothness setting.
Robustness to Unbounded Smoothness of Generalized SignSGD
We also compare these algorithms with popular optimizers on a set of deep learning tasks, observing that we can match the performance of Adam while beating the others.
Fast Composite Optimization and Statistical Recovery in Federated Learning
This paper investigates a class of composite optimization and statistical recovery problems in the FL setting, whose loss function consists of a data-dependent smooth loss and a non-smooth regularizer.
SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning
The best MTL optimization methods require individually computing the gradient of each task's loss function, which impedes scalability to a large number of tasks.
Multi-Task Learning with Deep Neural Networks: A Survey
Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are simultaneously learned by a shared model.
