Privacy-Preserving AI for Enterprise Data Collaboration through Federated Cloud Computing Architectures

Abstract

Enterprise organizations increasingly depend on data-driven artificial intelligence to improve decision-making, automation,
forecasting, customer services, fraud detection, healthcare analytics, and operational efficiency. However, valuable
enterprise data are frequently distributed across departments, business units, geographical locations, cloud providers,
and organizational boundaries. Conventional centralized AI approaches require organizations to transfer data to a common
repository, creating substantial privacy, security, governance, regulatory, and ownership concerns. Privacy-preserving
AI provides an alternative by enabling organizations to collaboratively train or use AI models without directly sharing
sensitive raw datasets. Federated learning is particularly relevant because model training can occur locally while only
selected model parameters, updates, or privacy-protected information are communicated to a coordinating infrastructure.
When implemented through federated cloud computing architectures, this approach can provide scalable computational
resources, distributed orchestration, secure communication, and flexible collaboration among enterprise participants.
Nevertheless, federated AI does not automatically guarantee privacy because model updates may reveal information,
participants may behave maliciously, and centralized aggregation services can become attractive targets. Additional
techniques such as secure aggregation, differential privacy, encryption, trusted execution environments, access
control, and robust aggregation may therefore be necessary. This research examines a privacy-preserving AI framework
for enterprise data collaboration using federated cloud computing. The proposed methodology combines literature analysis,
requirements identification, architectural design, prototype development, experimental evaluation, privacy-risk assessment,
and expert validation. The study focuses on privacy, model utility, communication efficiency, scalability, security,
and regulatory considerations to establish an enterprise-oriented architecture capable of enabling collaborative intelligence
while reducing the need for direct exchange of sensitive organizational data.