Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
<|reference_start|>Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods: Modern deep neural networks often require distributed training with many workers due to their large size. As worker numbers increase, communication overheads become the main bottleneck in data-parallel minibatch stochastic gradient methods with per-iteration gradient synchronization. Local gradient methods like Local SGD reduce communication by only syncing after several local steps. Despite understanding their convergence in i.i.d. and heterogeneous settings and knowing the importance of batch sizes for efficiency and generalization, optimal local batch sizes are difficult to determine. We introduce adaptive batch size strategies for local gradient methods that increase batch sizes adaptively to reduce minibatch gradient variance. We provide convergence guarantees under homogeneous data conditions and support our claims with image classification experiments, demonstrating the effectiveness of our strategies in training and generalization.<|reference_end|>
@article{lau2024communication-efficient,
    title={Communication-Efficient Adaptive Batch Size Strategies for Distributed
  Local Gradient Methods},
    author={Tim Tsz-Kit Lau and Weijian Li and Chenwei Xu and Han Liu and Mladen
  Kolar},
    journal={arXiv preprint arXiv:2406.13936},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13936},
    primaryClass={stat.ML cs.LG math.OC}
}