Neural Compression Techniques for Distributed Deep Learning Training

Abstract

Neural compression stands at the forefront of advancing distributed deep learning by markedly reducing the communication burden inherent in synchronizing model updates across

geographically dispersed compute nodes. Traditional distributed training systems struggle with the exponential growth of model parameters—often into the billions—leading to network

saturation, increased iteration times, and diminished overall throughput. In response, a suite of compression techniques—including gradient quantization, sparsification, low-rank factorization, and randomized sketching—has been proposed to encode updates in compact forms. However, existing methods typically target individual aspects of the compression–accuracy trade-off, lack adaptability to fluctuating network conditions, and require manual hyperparameter tuning.

This work introduces a cohesive, adaptive compression framework that synergistically combines error-feedback sparsification with a learned quantization scheduler. Our approach dynamically modulates sparsity ratios based on real-time gradient variance estimation, while a lightweight neural controller assigns per-layer bitwidths to balance precision and bandwidth. The dual mechanism ensures that compression noise remains bounded and correctable, thereby preserving convergence stability. We validate our framework across vision and language benchmarks— ResNet-50 on CIFAR-10/ImageNet and LSTM/Transformer on PTB/WikiText-2—under

simulated network environments ranging from 10 Mbps to 1 Gbps with varying latency profiles. Experimental results demonstrate up to a 10× reduction in total communication volume with less than 1% drop in top-1 accuracy, alongside a 35% improvement in end-to-end training throughput under constrained links.

Beyond empirical gains, we provide a concise theoretical analysis, establishing convergence guarantees in the presence of compounded compression operators and error-feedback loops. Our findings underscore the practicality of hybrid, learning-based compression in real-world deployments and lay the groundwork for future extensions that incorporate straggler mitigation, privacy assurances, and autonomous network profiling.