Federated Generative AI Framework for Privacy-Preserving Cloud Computing and Edge-Oriented Data Governance

Abstract

Generative AI methods—such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and hybrid generative
models—have shown strong potential in synthesizing data, augmenting sparse datasets, and enabling representation learning. Yet, their
deployment in sensitive and distributed environments (e.g. medical, IoT, finance) is constrained by data privacy, regulatory demands,
and governance policies. Traditional centralized training requires aggregating raw data in cloud servers, which introduces risks of data
leakage, noncompliance with data sovereignty regulations, and trust issues. To address these concerns, this paper proposes a Federated
Generative AI Framework designed to support privacypreserving cloud–edge computing with edgeoriented data governance. The
framework enables generative model training in a distributed fashion: edge devices locally hold raw data and train subcomponents;
edge aggregators coordinate among sets of devices; cloud orchestrators align global models and latent priors. Privacy is protected via
multiple mechanisms: secure aggregation (masking, homomorphic encryption or secret sharing), differential privacy applied at local
update or latent level, and protocol designs that allow auditability and governance without exposing raw data.
Key contributions include:
A multitier architecture (device ↔ edge aggregator ↔ cloud coordinator) that supports hierarchical model decomposition and reduces
communication overhead.
Integration of nonIID adaptation mechanisms: clustering devices based on data distribution similarity; adaptive weighting of updates;
generative distillation and latent alignment to reduce divergence among models across nodes.
Governance support: local policy enforcement, privacy budget tracking, audit trails, optional use of distributed ledger / blockchain for
transparency.
Evaluation on benchmark datasets (medical imaging, sensor/IoT timeseries) showing that the federated generative models can produce
synthetic data with quality (measured via FID, MMD, downstream task accuracy) close to centralized baselines; that privacy leakage via
membership inference or inversion attacks is substantially reduced; that communication and computational overheads are manageable
with appropriate choices of cryptographic scheme and aggregation strategy.
Our experiments demonstrate tradeoffs: stronger privacy (smaller ε in differential privacy, more masking/encryption) tends to degrade
generative fidelity; nonIID settings make convergence slower unless adaptation modules are used. Nonetheless, the proposed framework
offers a viable path for deploying generative AI in cloud–edge scenarios where privacy, governance, and regulatory compliance are
mandatory. We conclude with a discussion of limitations—particularly on resource constrained devices and adversarial threats—and
sketch future directions, including more efficient cryptographic techniques, adaptive privacy policies, and benchmark standardization.