Federated Deep Learning for Privacy-Preserving Analytics in Distributed Data Ecosystems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I6P101Keywords:
Federated Learning, Privacy-Preserving Machine Learning, Differential Privacy, Secure Aggregation, Non-IID Data, Robust Aggregation, Edge/IoT Analytics, MLOps, Explainability, Data SovereigntyAbstract
Federated deep learning (FDL) enables collaborative model training across data silos without centralizing raw data, addressing the legal, ethical, and operational barriers that constrain modern analytics. This work presents a privacy-preserving FDL framework for distributed data ecosystems spanning enterprises, hospitals, financial institutions, and edge/IoT networks. The framework combines secure aggregation with differential privacy to bound information leakage from model updates, and supports optional hardware trusted execution environments and homomorphic encryption for high-sensitivity use cases. To cope with real-world heterogeneity, we incorporate personalization layers and client-adaptive optimization to mitigate non-IID data skew, stragglers, and intermittent connectivity. Communication efficiency is improved via update sparsification and quantization, coordinated with server-side momentum and periodic aggregation. Robustness is strengthened through anomaly-resilient aggregation and poisoning/backdoor defenses informed by update attribution and reputational scoring. The architecture integrates with MLOps pipelines for auditability, lineage, and policy enforcement, and exposes explainability artifacts (e.g., post-hoc local explanations) to support risk and compliance reviews. We validate the approach through cross-silo and cross-device scenarios, demonstrating scalable convergence under realistic participation rates and privacy budgets while maintaining competitive accuracy relative to centralized baselines. The result is a practical blueprint for organizations to unlock multi-party insights such as fraud detection, medical risk stratification, and demand forecasting without moving sensitive data, thereby aligning innovation with privacy regulations and data sovereignty requirements
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