Adaptive Immunization Against Federated Data Poisoning: A Sequential Trust-Spectral Intent Framework for Robust Distributed Learning Process

Abstract

Increasing dependence on scattered data pipelines makes modern learning systems vulnerable to data poisoning in federated situations where raw data never centralizes and malicious updates can subtly degrade global models. Current defenses isolate anomalies in one training round, employ static thresholds, or delete suspicious clients, resulting in brittle protection, delayed detection, and model accuracy erosion. They react without understanding or adapting. This sequential, biologically inspired protection design prioritizes analytical structure over surface filtering. Topology-Aware Federated Data Provenance Graphing (TA-FDPG) calculates a trust vector from client update lineage and temporal behaviour to avoid round-wise detector blindness. In Trust-Weighted Spectral Gradient Purification (TW-SGP), singular value decomposition lowers poisoning-aligned gradient components instead of deleting them, preserving learning. Purified gradients feed Latent Behavioral Intent Extraction Network (LB IEN), which extracts latent attack intent signatures to provide the defense a prediction edge. RAMPE uses intent cues instead of heuristics to train effective defense tactics in real time scenarios. Finally, DP IMF creates a powerful, insulated global model from local models and policy-guided privacy noise sets. These methods move poisoned federated learning systems from passive detection to adaptive, anticipatory defense by reducing attack success, reaction latency, and clean accuracy for different scenarios.