  During typical gradient-based training of deep neural networks, all of the
model's parameters are updated at each iteration. Recent work has shown that it
is possible to update only a small subset of the model's parameters during
training, which can alleviate storage and communication requirements. In this
paper, we show that it is possible to induce a fixed sparse mask on the model's
parameters that selects a subset to update over many iterations. Our method
constructs the mask out of the $k$ parameters with the largest Fisher
information as a simple approximation as to which parameters are most important
for the task at hand. In experiments on parameter-efficient transfer learning
and distributed training, we show that our approach matches or exceeds the
performance of other methods for training with sparse updates while being more
efficient in terms of memory usage and communication costs. We release our code
publicly to promote further applications of our approach.
