Krishna Yadav

In next-generation networks, the integration of edge intelligence and large models has transformed real-time applications such as intrusion detection and anomaly monitoring. However, standard federated learning approaches (e.g., FedAvg) struggle with data heterogeneity across edge devices, often yielding suboptimal performance—especially for minority classes. To address this challenge, we propose an adaptive reparameterization framework called FedGRAA (Federated Gradient-based Adaptive Aggregation). FedGRAA employs an adaptive, gradient-driven weighting strategy that dynamically adjusts each edge device's contributions based on its data distribution, ensuring that clients with smaller datasets still wield a meaningful influence on the global model. This design not only enhances detection accuracy for underrepresented classes but also preserves data privacy by keeping raw data local to the devices. Experimental results show that FedGRAA outperforms FedAvg, increasing accuracy from 80% to 88.88% for minority-class detection. Our findings highlight the importance of adaptive reparameterization in advancing federated learning for edge devices within the evolving landscape of next-generation networks.

Federated learning has emerged as a promising solution for collaborative learning of IoT-based attacks without requiring data sharing. However, system heterogeneity among IoT devices results in diverse feature spaces due to varying device types, protocols, and data formats. This leads to differences in how attacks manifest across devices, causing traditional FL systems to struggle with feature space alignment and model convergence. While common attacks exhibit diverse feature spaces, they share fundamental characteristics. To address this, we utilized an autoencoder at the client side to find an optimal latent representation that captures attack similarities, transforming heterogeneous feature spaces into a uniform latent space. Using the CICIDS 2017 and UNSW datasets, we demonstrated that this approach not only resolves feature space diversity but also enhances model performance, outperforming individual models in 75% of classification scenarios through improved capture of shared attack characteristics.

In next-generation networks, the integration of edge intelligence and large models has transformed real-time applications such as intrusion detection and anomaly monitoring. However, standard federated learning approaches (e.g., FedAvg) struggle with data heterogeneity across edge devices, often yielding suboptimal performance—especially for minority classes. To address this challenge, we propose an adaptive reparameterization framework called FedGRAA (Federated Gradient-based Adaptive Aggregation). FedGRAA employs an adaptive, gradient-driven weighting strategy that dynamically adjusts each edge device's contributions based on its data distribution, ensuring that clients with smaller datasets still wield a meaningful influence on the global model. This design not only enhances detection accuracy for underrepresented classes but also preserves data privacy by keeping raw data local to the devices. Experimental results show that FedGRAA outperforms FedAvg, increasing accuracy from 80% to 88.88% for minority-class detection. Our findings highlight the importance of adaptive reparameterization in advancing federated learning for edge devices within the evolving landscape of next-generation networks.

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