# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 6 Conclusions  
This study was motivated by the long-standing challenge of developing alternatives to the back-propagation algorithm that are both biologically plausible and resource-efficient. Although the Forward-Forward algorithm offers a promising direction, its reliance on handcrafted input samples and non-standard loss functions, as well as its training instability, has limited its practical applicability.  
To address these limitations, we proposed the Local Back-Propagation framework, which employs independent, layer-wise unsupervised learning in place of the original Forward-Forward mechanism. This design eliminates the need for specially constructed positive and negative samples and allows the use of standard input formats and loss functions. Furthermore, it significantly improves training stability while maintaining compatibility with conventional deep learning pipelines.  
Experimental results confirmed the effectiveness of the proposed approach, demonstrating competitive performance compared to standard MLP models and highlighting its notable memory efficiency. This advantage makes LBP particularly suitable for deployment in resource-constrained or decentralized environments such as federated learning.  
For future work, we aim to reduce the performance gap between LBP and end-to-end trained models by exploring hybrid learning strategies that incorporate minimal global coordination. Additionally, we plan to investigate advanced unsupervised objectives within individual LBP cells and validate the proposed method on real-world edge devices to further evaluate its practical utility.  
Conceptualization, T.H.; Methodology, T.H.; Software, T.H.; Validation, T.H. and H.S.; Investigation, T.H.; Resources, T.H.; Data Curation, T.H. and H.S.; Writing--Original Draft Preparation, T.H.; Writing--Review and Editing, H.S.; Visualization, H.S.; Supervision, S.J.; Project Administration, S.J.; Funding Acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00155857), Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2025-0055621731482092640101) and research fund from Chung National University.  
Not applicable.  
The datasets used in this study include MNIST, CIFAR-10, and CIFAR-100. The MNIST dataset is publicly available at [[https://github.com/pytorch/vision/blob/main/torchvision/datasets/mnist.py](https://github.com/pytorch/vision/blob/main/torchvision/datasets/mnist.py)]([https://github.com/pytorch/vision/blob/main/torchvision/datasets/mnist.py](https://github.com/pytorch/vision/blob/main/torchvision/datasets/mnist.py)) (accessed on 22 July 2025). The CIFAR-10 and CIFAR-100 datasets are publicly available at [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 22 July 2025). The source code used in this study will be made available on [[https://github.com/GGoMaAI/LocalBackPropagation/tree/Dev](https://github.com/GGoMaAI/LocalBackPropagation/tree/Dev)]([https://github.com/GGoMaAI/LocalBackPropagation/tree/Dev](https://github.com/GGoMaAI/LocalBackPropagation/tree/Dev)) (accessed on 22 July 2025; version 1.0, Daejeon, Republic of Korea)  
During the preparation of this manuscript, the authors used ChatGPT (OpenAI, GPT-4) for the purpose of English editing. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
Author Sangkeun Jung is a founder of Eureka AI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.