An Extensive Study on D2C: Overfitting Remediation in Deep Learning Using a Decentralized Approach

2024-11-24

arXiv.org

AI Summary

Deep learning models suffer from overfitting under noisy data, outliers, and small-sample regimes. Existing Divide2Conquer (D2C) methods aggregate sub-models via uniform or heuristic weighting, ignoring their heterogeneous generalization capabilities. Method: We propose an enhanced D2C framework: (i) decentralized data partitioning to construct heterogeneous subsets; (ii) training homogeneous sub-models; and (iii) a generalization-aware dynamic parameter aggregation mechanism—requiring no explicit regularization or data augmentation. Contributions/Results: We theoretically establish convergence and uncover generalization mechanisms via decision-boundary analysis and loss modeling. Extensive experiments on multiple benchmark datasets demonstrate significant improvements over baselines, especially with large-scale data where generalization gains are most pronounced. Our method is orthogonal to mainstream anti-overfitting techniques and enables seamless integration. The implementation is publicly available.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Overfitting remains a significant challenge in deep learning, often arising from data outliers, noise, and limited training data. To address this, we propose Divide2Conquer (D2C), a novel technique to mitigate overfitting. D2C partitions the training data into multiple subsets and trains identical models independently on each subset. To balance model generalization and subset-specific learning, the model parameters are periodically aggregated and averaged during training. This process enables the learning of robust patterns while minimizing the influence of outliers and noise. Empirical evaluations on benchmark datasets across diverse deep-learning tasks demonstrate that D2C significantly enhances generalization performance, particularly with larger datasets. Our analysis includes evaluations of decision boundaries, loss curves, and other performance metrics, highlighting D2C's effectiveness both as a standalone technique and in combination with other overfitting reduction methods. We further provide a rigorous mathematical justification for D2C's underlying principles and examine its applicability across multiple domains. Finally, we explore the trade-offs associated with D2C and propose strategies to address them, offering a holistic view of its strengths and limitations. This study establishes D2C as a versatile and effective approach to combating overfitting in deep learning. Our codes are publicly available at: https://github.com/Saiful185/Divide2Conquer.

Problem

• Addressing overfitting caused by data outliers and limited training data
• Improving aggregation of subset models by considering their generalization capabilities
• Dynamically weighting model contributions based on validation performance and uncertainty

Innovation

• Dynamic weighting of subset models using validation performance
• Aggregation based on both accuracy and prediction uncertainty
• Improves generalization by learning from confident edge models