Self-Healing AI Models Using Continual Learning Algorithms

Abstract

Self‑healing AI models represent a transformative step forward in the deployment of intelligent systems capable of autonomous maintenance and uninterrupted operation in dynamic or adversarial settings. By integrating continual learning algorithms—namely Elastic Weight Consolidation (EWC), Memory‑Aware Synapses (MAS), and Gradient Episodic Memory (GEM)—these models detect anomalies such as data distribution shifts, label noise, or adversarial perturbations, and subsequently enact targeted parameter updates to restore performance without manual retraining. In this study, we develop a modular self‑healing pipeline comprising fault detection, online adaptation, and post‑recovery evaluation. Using three benchmark tasks (handwritten digit classification on MNIST, object recognition on CIFAR‑10, and synthetic sensor time‑series forecasting), we simulate faults at mid‑training and measure performance recovery across 1,200 experimental runs. Statistical analyses—including one‑way ANOVA and Tukey’s HSD—demonstrate that GEM‑based healing achieves the most robust recovery, yielding a 12.8% (±1.5%) higher post‑fault accuracy in classification tasks and a 40% reduction in forecasting MSE relative to non‑healing baselines (p < 0.001). EWC offers rapid adaptation with minimal catastrophic forgetting, while MAS balances recovery speed and memory efficiency. Beyond empirical gains, we present ablation studies that elucidate how buffer size, regularization strength, and fault detector sensitivity influence recovery dynamics. We further explore computational overhead, showing that GEM’s replay constraints increase training time by approximately 25%, whereas EWC and MAS incur only marginal extra cost. Finally, we illustrate practical deployment considerations by discussing on‑device implementation, safety monitoring integration, and regulatory compliance in safety‑critical domains. Our results confirm that self‑healing architectures underpinned by continual learning not only enhance reliability and longevity but also open new avenues for reducing operational costs and human intervention in AI maintenance, with broad implications for autonomous vehicles, industrial IoT, and healthcare diagnostics.