Federated Deep Learning with Privacy Guarantees for Large-Scale Distributed AI Applications
DOI:
https://doi.org/10.15662/IJEETR.2023.0506005Keywords:
Federated Learning, Deep Learning, Privacy Preservation, Differential Privacy, Secure Aggregation, Homomorphic Encryption, Distributed AI, Edge Computing, IoT, Large-Scale SystemsAbstract
The rapid proliferation of data-generating devices and large-scale intelligent systems has led to an exponential increase in distributed data across mobile, embedded, and IoT environments. Traditional centralized machine learning approaches rely heavily on aggregating raw data to central servers, raising significant privacy, security, and communication-efficiency concerns. Federated Deep Learning (FDL) has emerged as a transformative paradigm enabling collaborative model training without the need to share sensitive local datasets. Despite its promise, several unresolved challenges persist, including data heterogeneity, communication overhead, adversarial threats, and insufficient formal privacy guarantees. This research paper presents a comprehensive framework for Federated Deep Learning with Privacy Guarantees (FDL-PG) designed explicitly for large-scale distributed AI applications such as smart healthcare systems, edge-based autonomous vehicles, intelligent IoT ecosystems, and industrial automation networks.
The proposed framework integrates advanced deep learning architectures with rigorous privacy-preserving techniques, including secure aggregation, differential privacy, homomorphic encryption, and adversarial robustness strategies. By incorporating secure aggregation protocols, the system ensures that individual model updates remain concealed even from the central coordinating server. Differential privacy mechanisms further introduce calibrated noise to gradients and model parameters, thereby preventing sensitive information leakage while maintaining task accuracy. Homomorphic encryption plays a complementary role by enabling computation on encrypted updates, ensuring data confidentiality even during intermediate processing steps.
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