FLVaccin: Unbalanced Hierarchical Federated Learning with Vaccination-Calibrated Adaptive Quarantine for Robust Poisoning Defense

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Federated learning (FL) enables collaborative training without centralizing raw data, yet malicious clients can poison local data or updates and degrade the global model. We present FLVaccin, a hierarchical framework on an unbalanced tree in which every non-root node aggregates and hosts local clients—to our knowledge, the first design not confining all clients to the deepest level; only the root aggregates with zero local clients. All experiments use personalized federated learning (FedPer): shared feature layers are federated bottom-up, while each client keeps a private classifier head. FLVaccin combines two defenses: (i) node-level vaccination, mixing CIFAR-100 images into CIFAR-10 shards, and (ii) depth- and round-adaptive quarantine, whose per-client tolerances are set from vaccination mean-accuracy calibration (30 single-round trials across injection-intensity bands); clients exceeding allowed metric swings are temporarily excluded, while the root rejects global backbone updates with deteriorating validation trends. Implemented with MobileNetV2/FedPer and Dirichlet Non-IID partitioning (α = 0.5), 100 clients federate across 25 nodes over four levels. Without defense, 130 attacks collapse test accuracy from 77.4% to 21.0%; with our defense mechanism, 535 attacks yield 77.3% (k-fold 76.5%±0.4%), within 2.6 percentage points of the clean baseline (96.8% of clean accuracy), showing practical tree-aware robustness while preserving privacy.