Federated Learning in Cloud Environments for Privacy-Preserving Artificial Intelligence Solutions

TITLE	Federated Learning in Cloud Environments for Privacy-Preserving Artificial Intelligence Solutions
ABSTRACT	In many modern AI applications (healthcare, finance, IoT, etc.), sensitive data is distributed among multiple clients, institutions, or devices. Traditional centralized machine learning requires pooling raw data in one place (e.g. a cloud server), which raises serious privacy, security, and legal concerns. Federated Learning (FL) has emerged as a promising paradigm that allows collaborative model training across decentralized nodes such that raw data remains local, hence preserving privacy. This paper investigates the integration of federated learning in cloud environments to provide privacy preserving AI solutions. We survey architectural designs, privacy enhancing techniques (such as secure aggregation, differential privacy, homomorphic encryption), communication strategies, and real world deployments. We present a reference framework for deploying FL in the cloud, considering issues of scalability, heterogeneity of clients, communication overhead, and regulatory compliance. Our methodology includes implementing prototype FL systems over multiple cloud providers, simulating non IID data distributions, applying privacy mechanisms, and evaluating on metrics: model accuracy, convergence speed, communication cost, privacy leakage risk, and resource consumption. Experimental results show that FL in cloud setups can achieve model accuracy close to centralized baselines (within ~2 5% drop), while drastically reducing risk of data exposure. Communication cost can be managed by model compression and asynchronous update protocols. However, there are trade offs: privacy mechanisms tend to reduce accuracy or slow convergence; heterogeneous client capacities lead to stragglers; regulatory and security threats remain. We discuss these trade offs and propose design guidelines for practitioners: hybrid cloud edge architectures, adaptive privacy budgets, efficient aggregation methods, robust client selection. In conclusion, federated learning in cloud environments offers a viable path to privacy preserving AI, enabling compliance with data protection laws while still delivering high performance. Future work includes more deployment in regulated sectors, standardization of privacy metrics, better robustness to adversarial threats, and developing methods to reduce overhead further.
AUTHOR	William Brown Faculty of Electrical and Computer Engineering, York University, Toronto, Canada
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1206009
PDF	9_Federated Learning in Cloud Environments for Privacy-Preserving Artificial Intelligence Solutions.pdf
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