DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models (2404.08079v1)

Abstract: Recent advances in decentralized deep learning algorithms have demonstrated cutting-edge performance on various tasks with large pre-trained models. However, a pivotal prerequisite for achieving this level of competitiveness is the significant communication and computation overheads when updating these models, which prohibits the applications of them to real-world scenarios. To address this issue, drawing inspiration from advanced model merging techniques without requiring additional training, we introduce the Decentralized Iterative Merging-And-Training (DIMAT) paradigm--a novel decentralized deep learning framework. Within DIMAT, each agent is trained on their local data and periodically merged with their neighboring agents using advanced model merging techniques like activation matching until convergence is achieved. DIMAT provably converges with the best available rate for nonconvex functions with various first-order methods, while yielding tighter error bounds compared to the popular existing approaches. We conduct a comprehensive empirical analysis to validate DIMAT's superiority over baselines across diverse computer vision tasks sourced from multiple datasets. Empirical results validate our theoretical claims by showing that DIMAT attains faster and higher initial gain in accuracy with independent and identically distributed (IID) and non-IID data, incurring lower communication overhead. This DIMAT paradigm presents a new opportunity for the future decentralized learning, enhancing its adaptability to real-world with sparse and light-weight communication and computation.

• The paper introduces a novel decentralized framework using activation matching to merge local deep models, reducing communication overhead and accelerating convergence.
• The paper integrates standard optimization methods like SGD with model merging to handle non-IID data and ensure robust training.
• The paper demonstrates through theoretical and empirical analysis that DIMAT achieves faster convergence, scalable performance, and high accuracy across diverse datasets.

Decentralized Iterative Merging-And-Training (DIMAT) Paradigm for Deep Learning Models

Introduction

The Decentralized Iterative Merging-And-Training (DIMAT) framework proposes an innovative approach to decentralized deep learning, addressing key limitations associated with significant communication and computation overheads in model updating, particularly for large models such as VGG and ResNet. This paper introduces a novel method that incorporates advanced model merging techniques, known as activation matching, to periodically merge local models trained independently by different agents.

Methodology

Activation Matching Methodology

Activation matching plays a central role in DIMAT by aligning model activations of neighboring agents before merging. This process involves treating the synchronization of network models across different nodes as a linear assignment problem, solvable efficiently through existing algorithms. Key steps include:

• Computing cross-correlations of activations to establish a correspondence between units of different models.
• Optimizing the permutation of layers to minimize the Frobenius norm of the difference between matched activations, leading to a synergistic update that respects the underlying data distributions and model complexities.

Algorithmic Details

DIMAT can be integrated with standard first-order methods such as stochastic gradient descent (SGD), momentum SGD, and Adam. The framework modifies the traditional learning process by including a merging phase, based on activation matching, to synchronize and update local models. This modified learning process not only reduces the required communication rounds but also ensures that models converge efficiently even under non-IID data distribution conditions.

Main Results

Theoretical Insights

Theoretical analyses confirm that DIMAT enhances learning speed and model accuracy with lower communication costs. Specifically, the framework:

• Achieves faster convergence by leveraging a tighter convergence bound, attributed to the reduced spectral gap effected through permutation-enhanced merging.
• Demonstrates robustness across different network topologies and scales linearly with the increase in the number of agents without significant performance degradation.

Empirical Validation

Empirical studies across standard datasets like CIFAR-10, CIFAR-100, and Tiny ImageNet using common network architectures validate the theoretical claims. DIMAT consistently outperforms existing decentralized training methods, particularly in handling non-IID data distributions efficiently. The merging mechanism introduces minimal overhead and adapts dynamically to the data and network characteristics, preserving learning accuracy and speed.

Implications and Future Research

The success of the DIMAT framework suggests several avenues for future research:

• Exploration of Permutation Techniques: Investigating various model merging and permutation strategies could further optimize the consensus among decentralized agents.
• Quantization and Model Merging: Integrating quantization techniques with model merging may offer new ways to reduce communication costs dramatically.
• Scalability with Non-IID Data: Addressing the scalability issues observed with non-IID data by potentially exploring hybrid approaches that combine centralized and decentralized elements to maintain balance.

The DIMAT paradigm presents a promising new direction for scalable, efficient, and robust decentralized learning, potentially transformative for real-world applications where data privacy, security, and access rights are crucial. Future expansions could refine its adaptability and utility, paving the way for broader adoption in distributed machine learning tasks.