Federated Deep Hashing with Margin-Scalable Semantic Constraints for Privacy-Preserving Distributed Image Retrieval - Samuel D. Wells; Dactaor Wood; Sean L. Bell; Summer Prasad (Bioinformatics Insights and Analytics, 2026)
The proliferation of image data across geographically distributed and institutionally heterogeneous repositories has created an urgent demand for retrieval systems that simultaneously guarantee high-fidelity semantic search and rigorous data privacy. Federated deep hashing, which marries the representational power of deep learning with compact binary hash codes, offers a promising pathway, yet its real-world deployment is constrained by difficulties in controlling hash code discrimination, preserving semantic alignment under non-identically distributed data, and ensuring compliance with evolving data governance regimes. This paper provides a comprehensive system-level analysis of a federated deep hashing architecture augmented with margin-scalable semantic constraints. By dynamically adjusting the similarity margins that govern hash space partitioning, the framework enables nuanced trade-offs between compactness, retrieval precision, and resilience to statistical heterogeneity across siloed data sources. We examine the structural design of federated hash learning pipelines, dissect the role of margin-scalable constraints as a governance instrument for semantic fidelity, and explore their interplay with communication efficiency, model compression, and privacy-preserving aggregation. Beyond technical architecture, the paper discusses operational infrastructure, sustainability concerns, fairness under disparate data distributions, and the alignment of federated hashing with international data protection frameworks. Through a cross-domain lens, we argue that margin scalability transforms a purely accuracy-oriented mechanism into a socio-technical control surface, empowering system operators to balance competing objectives in large-scale, privacy-sensitive image retrieval ecosystems. The analysis is grounded in contemporary advances in deep hashing, federated learning, secure aggregation, and differential privacy, providing a forward-looking perspective on resilient, policy-compliant image indexing infrastructures.