A Multi-Modal Expert-Driven ISAC Framework with Hierarchical Federated Learning for 6G Network

TL;DR

This paper introduces a new AI framework for edge devices that improves data privacy and performance in 6G networks using expert-guided data generation and hierarchical learning.

Contribution

A novel expert-driven AC-GAN framework for multi-modal federated learning that addresses non-IID data, privacy, and resource constraints.

Findings

The framework achieves a precision of 0.89 in practical multi-modal settings.

It outperforms federated baselines in accuracy and convergence stability.

Communication overhead is significantly reduced compared to classical methods.

Abstract

We propose a novel Expert-Driven Conditional Auxiliary Classifier Generative Adversarial Network (AC-GAN) framework tailored for heterogeneous multi-modal federated learning at edge AI devices such as the NVIDIA Jetson Orin Nano. Unlike prior works that assume idealized distributions or rely on centralized data, our approach jointly addresses statistical non-IID data, model heterogeneity, privacy protection, and resource constraints through an expert-guided training pipeline and hierarchical model updates. Specifically, we introduce a collaborative synthesis and aggregation mechanism where local experts guide conditional data generation, enabling realistic data augmentation on resource-constrained edge nodes and enhancing global model generalization without sharing raw data. Through hierarchical updates between client and server levels, our method mitigates bias from skewed local distributions and significantly reduces communication overhead compared to classical federated averaging baselines. We demonstrate that while “perfect precision” is theoretically unattainable under non-IID and real-world conditions, our framework achieves substantially improved precision and false positive trade-offs (e.g., precision 0.89) relative to benchmarks, validating robustness in practical multi-modal settings. Extensive experiments across synthetic and real datasets show that the proposed AC-GAN approach consistently outperforms federated baselines in accuracy, convergence stability, and privacy preservation. Our results suggest that expert-guided conditional generative modeling is a promising direction for scalable, privacy-aware edge intelligence.

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