FedCAMP-IDS: a federated cluster-aware memory-augmented prototypical network for intrusion detection in heterogeneous IoT environments

Scenario-Based Federated Learning for Privacy-Aware IoT Botnet Intrusion Detection

Federated learning (FL) is a promising approach for IoT intrusion detection because it enables distributed clients to collaboratively train models without pooling raw network-flow records. However, IoT traffic is often heterogeneous across monitoring sites, devices, and attack scenarios, which can degrade federated mod...

Adaptive Federated Baseline K-Means for Lightweight IoT Intrusion Detection: Auto-Thresholding and Robust Statistics Aggregation

AF-BKM is presented, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, and identifies merge-induced precision decay under non-IID workers as an open gap.

HEDRA-IDS: A Hierarchical Edge-Driven Residual Attention Framework for SDN-Enabled Intrusion Detection in Wireless Sensor Networks

FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks

Federated Bandit Intrusion Detection (FBID), a novel adaptive PFL framework to address this limitation through server-side personalization control, employs a contextual multi-armed bandit at the server to dynamically regulate each client's local training intensity according to its observed behavior and update quality.

AF-IoTS: An Adaptive Trust-Aware Federated Architecture for Scalable Intrusion Detection in Heterogeneous IoT Environments

The Internet of Things (IoT) generates massive, privacy-sensitive traffic across heterogeneous, resource-constrained devices, making centralized intrusion detection systems (IDS) increasingly impractical due to scalability, latency, and privacy limitations. Existing federated learning (FL) based IDS solutions partially...

A Privacy-Preserving Federated Learning Framework for Intrusion Detection in Healthcare IoT Environments

PPFL-IDS combines federated model aggregation with differential privacy noise injection and secure aggregation protocols to train a lightweight gradient-boosted ensemble IDS without exposing local device data, demonstrating that strong privacy guarantees and high detection accuracy can be achieved simultaneously in fed...