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NetworkFF: Unified Layer Optimization in Forward-Only Neural Networks

Salar Beigzad

TL;DR

The paper tackles the memory and plausibility limitations of backpropagation by leveraging Forward-Forward learning. It introduces Collaborative Forward-Forward (CFF) that enables inter-layer coordination via fixed and adaptive collaboration schemes. Extensive experiments on MNIST and Fashion-MNIST show that CFF improves accuracy over baseline FF, with adaptive CFF providing the largest gains. The results suggest that inter-layer collaboration preserves memory efficiency and biological plausibility while boosting convergence and performance, with implications for neuromorphic and energy-constrained AI.

Abstract

The Forward-Forward algorithm eliminates backpropagation's memory constraints and biological implausibility through dual forward passes with positive and negative data. However, conventional implementations suffer from critical inter-layer isolation, where layers optimize goodness functions independently without leveraging collective learning dynamics. This isolation constrains representational coordination and limits convergence efficiency in deeper architectures. This paper introduces Collaborative Forward-Forward (CFF) learning, extending the original algorithm through inter-layer cooperation mechanisms that preserve forward-only computation while enabling global context integration. Our framework implements two collaborative paradigms: Fixed CFF (F-CFF) with constant inter-layer coupling and Adaptive CFF (A-CFF) with learnable collaboration parameters that evolve during training. The collaborative goodness function incorporates weighted contributions from all layers, enabling coordinated feature learning while maintaining memory efficiency and biological plausibility. Comprehensive evaluation on MNIST and Fashion-MNIST demonstrates significant performance improvements over baseline Forward-Forward implementations. These findings establish inter-layer collaboration as a fundamental enhancement to Forward-Forward learning, with immediate applicability to neuromorphic computing architectures and energy-constrained AI systems.

NetworkFF: Unified Layer Optimization in Forward-Only Neural Networks

TL;DR

The paper tackles the memory and plausibility limitations of backpropagation by leveraging Forward-Forward learning. It introduces Collaborative Forward-Forward (CFF) that enables inter-layer coordination via fixed and adaptive collaboration schemes. Extensive experiments on MNIST and Fashion-MNIST show that CFF improves accuracy over baseline FF, with adaptive CFF providing the largest gains. The results suggest that inter-layer collaboration preserves memory efficiency and biological plausibility while boosting convergence and performance, with implications for neuromorphic and energy-constrained AI.

Abstract

The Forward-Forward algorithm eliminates backpropagation's memory constraints and biological implausibility through dual forward passes with positive and negative data. However, conventional implementations suffer from critical inter-layer isolation, where layers optimize goodness functions independently without leveraging collective learning dynamics. This isolation constrains representational coordination and limits convergence efficiency in deeper architectures. This paper introduces Collaborative Forward-Forward (CFF) learning, extending the original algorithm through inter-layer cooperation mechanisms that preserve forward-only computation while enabling global context integration. Our framework implements two collaborative paradigms: Fixed CFF (F-CFF) with constant inter-layer coupling and Adaptive CFF (A-CFF) with learnable collaboration parameters that evolve during training. The collaborative goodness function incorporates weighted contributions from all layers, enabling coordinated feature learning while maintaining memory efficiency and biological plausibility. Comprehensive evaluation on MNIST and Fashion-MNIST demonstrates significant performance improvements over baseline Forward-Forward implementations. These findings establish inter-layer collaboration as a fundamental enhancement to Forward-Forward learning, with immediate applicability to neuromorphic computing architectures and energy-constrained AI systems.

Paper Structure

This paper contains 20 sections, 8 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Limitations of Backpropagation
  3. The Forward-Forward Algorithm
  4. Collaborative Forward-Forward Learning
  5. Contributions and Experimental Validation
  6. Related Work
  7. Predictive Coding and Alternative Learning Paradigms
  8. The Forward-Forward Algorithm
  9. Algorithm Implementation and Mathematical Framework
  10. Biological Plausibility and Interpretability Imperatives
  11. Problem Statement
  12. Proposed Methodology
  13. Training Protocol
  14. Experimental Design
  15. Evaluation Protocol
  16. ...and 5 more sections

Figures (4)

  • Figure 1: MNIST Final Training vs Test Accuracy Comparison across Forward-Forward variants. Adaptive CFF achieves highest performance with 94.1% training and 93.8% test accuracy, representing 1.8% improvement over baseline FF.
  • Figure 2: MNIST Training Accuracy Progress (Log Scale) during sequential layer training. Adaptive CFF exhibits sustained improvement reaching log-scale accuracy of 4.555, demonstrating superior convergence properties compared to baseline variants.
  • Figure 3: Fashion-MNIST Training Accuracy Progress (Log Scale) showing compressed accuracy ranges reflecting task complexity. Adaptive CFF maintains sustained learning through final phases, achieving peak log-scale accuracy of 4.4345.
  • Figure 4: Fashion-MNIST Final Training vs Test Accuracy Comparison demonstrating collaborative advantage on complex visual classification. Adaptive CFF achieves 83.0% test accuracy, representing 2.0% improvement over baseline FF.