Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication (1805.08768v1)

Abstract: Currently, progressively larger deep neural networks are trained on ever growing data corpora. As this trend is only going to increase in the future, distributed training schemes are becoming increasingly relevant. A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general. These challenges become even more pressing, as the number of computation nodes increases. To counteract this development we propose sparse binary compression (SBC), a compression framework that allows for a drastic reduction of communication cost for distributed training. SBC combines existing techniques of communication delay and gradient sparsification with a novel binarization method and optimal weight update encoding to push compression gains to new limits. By doing so, our method also allows us to smoothly trade-off gradient sparsity and temporal sparsity to adapt to the requirements of the learning task. Our experiments show, that SBC can reduce the upstream communication on a variety of convolutional and recurrent neural network architectures by more than four orders of magnitude without significantly harming the convergence speed in terms of forward-backward passes. For instance, we can train ResNet50 on ImageNet in the same number of iterations to the baseline accuracy, using $\times 3531$ less bits or train it to a $1\%$ lower accuracy using $\times 37208$ less bits. In the latter case, the total upstream communication required is cut from 125 terabytes to 3.35 gigabytes for every participating client.

• The paper presents SBC, a framework that integrates temporal sparsity, gradient binarization, and optimal encoding to drastically lower communication needs.
• It demonstrates up to a 40,000-fold reduction in communicated bits during training of models like ResNet50 on ImageNet.
• The methodology leverages error accumulation and advanced position encoding to maintain model accuracy despite severe compression.

Insightful Overview of "Sparse Binary Compression: Towards Distributed Deep Learning with Minimal Communication"

The paper "Sparse Binary Compression: Towards Distributed Deep Learning with Minimal Communication" presents an innovative approach to address the bandwidth limitations in distributed deep learning systems. The authors focus on reducing the communication overhead inherent in Distributed Stochastic Gradient Descent (DSGD), a fundamental algorithm in the training of extensive deep learning models. Given the exponential increase in both model sizes and datasets, reducing the communication cost between nodes is a critical area of research in distributed machine learning systems.

Core Contributions

The primary contribution of this work is the development of Sparse Binary Compression (SBC), a comprehensive framework combining several techniques to significantly reduce communication costs. SBC enhances existing compression methodologies by integrating communication delay, gradient sparsification, a novel binarization method, and optimal weight update encoding. This synergistic approach sets SBC apart by addressing all major components contributing to communication bandwidth in DSGD, defined as the total number of bits required for training iterations.

1. Communication Delay: Building on techniques like Federated Averaging, SBC introduces temporal sparsity by allowing clients to perform multiple iterations of SGD before communicating updates. This results in substantial communication frequency reductions without notable convergence speed disruptions.
2. Sparse Binarization: The paper employs an extreme form of gradient sparsification. It integrates a novel binarization technique where only the largest and smallest gradient updates, based on magnitude, are retained and averaged. This significantly reduces the number of value bits and positions the mean-centric quantization ahead of other simple quantization techniques.
3. Residual Accumulation: SBC retains compression efficiency by accumulating the error of previous iterations' compression, thus not losing critical gradient information, ensuring that training trajectories remain as optimal as possible.
4. Optimal Position Encoding: By enhancing how the positions of non-zero elements are communicated, using Golomb encoding, SBC minimizes the overhead typically associated with naive position encoding methods.

Results and Evaluation

Empirical evaluations demonstrate SBC's ability to drastically compress communication requirements—achieving up to a 40,000-fold reduction in communicated bits—while maintaining comparable or slightly degraded model accuracies. For instance, in training ResNet50 on ImageNet, SBC substantially reduced the required bits, cutting the communication volume per client from 125 terabytes to mere gigabytes. These results underpin the practical feasibility and effectiveness of SBC in bandwidth-constrained settings.

Implications and Forward-Looking Speculations

This paper addresses key challenges faced in distributed environments, especially those involving edge devices where bandwidth can be a limiting operative factor. The interplay between temporal sparsity and gradient sparsity presented in the paper hints at a nuanced understanding of communication efficiency—proposing adaptive schemes that tailor compression strategies dynamically based on training phases and networking conditions.

Looking forward, the implications of SBC could reflect broader in federated learning systems, privacy-preserving distributed algorithms, and any application requiring efficient distributed computations over constrained networks. It opens potential research avenues in optimizing adaptive compression frameworks, which can empirically determine optimal sparsity mixes for various real-world applications.

In conclusion, this work establishes SBC as a compelling contribution to effective distributed deep learning, propelling the field further in pursuit of efficient, scalable, and minimally communicative AI systems. As AI evolves, methodologies like SBC will remain at the forefront, enhancing model training efficacy in distributed environments.