We introduce a memory- and compute-efficient method for low-communication distributed training. Existing methods reduce communication by performing multiple local updates between infrequent global synchronizations. We demonstrate that their efficiency can be significantly improved by restricting backpropagation: instead of updating all the parameters, each node updates only a fixed subset while keeping the remainder frozen during local steps. This constraint substantially reduces peak memory usage and training FLOPs, while a full forward pass over all parameters eliminates the need for cross-node activation exchange. Experiments on a 1.3B-parameter language model trained across 32 nodes show that our method matches the perplexity of prior low-communication approaches under identical token and bandwidth budgets while reducing training FLOPs and peak memory.
@article{arxiv.2509.22418,
title = {Partial Parameter Updates for Efficient Distributed Training},
author = {Anastasiia Filippova and Angelos Katharopoulos and David Grangier and Ronan Collobert},
journal= {arXiv preprint arXiv:2509.22418},
year = {2025}
}