Date of Award

Summer 8-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical and Computer Engineering

Committee Director

Sachin Shetty

Committee Member

Chunsheng Xin

Committee Member

Danda Rawat

Committee Member

Daniel Takabi

Abstract

Machine learning models deployed in tactical environments rely on high-quality labeled datasets, which are often challenging, expensive, and time-consuming to obtain. In addition, formal techniques for assuring model performance under dynamic conditions remain limited. Transfer learning has emerged as a promising solution, but its effectiveness is limited by domain coverage and similarity between source and target datasets. This dissertation presents a comprehensive, modelagnostic Targeted Transfer Learning (TTL) framework that addresses transferability challenges between domains with minimal data, demonstrating how targeted sample selection improves adaptation across image classification, network security, and privacy-preserving collaborative settings. First, we develop the TTL framework for scenarios where direct access to datasets is restricted. This metadata-driven approach applies to a combinatorial coverage method to estimate domain correlation and guide sample selection. Evaluation on a satellite imagery dataset showed that targeted selection outperformed zero-shot transfer and random selection while conserving computational resources. Second, we investigate the TTL framework in settings where both datasets are directly available. This data-driven approach uses optimal transport distance to quantify distributional differences and guide selection of informative target samples. Validation across a broad set of benchmark image classification datasets demonstrated consistent improvements over conventional transfer learning. Third, we address network malware classification through Selective Targeted Transfer Learning (STTL), projecting source and target traffic data into a shared latent feature space and selecting relevant samples using a Kullback–Leibler divergence-based similarity measure. Experimental results across multiple attack-family scenarios showed that STTL achieved higher performance than conventional transfer learning, confirming that targeted selection generalizes across data types and domains. Finally, we extend the TTL framework through two decentralized privacy-preserving mechanisms. In a federated learning setting, clients share only summary statistics, and a server computes aggregation weights that enable knowledge transfer without exposing local data. In a blockchain-based setting, cryptographic verification secures model weights throughout the transfer process, while a smart-contract mechanism rewards high-quality participation. Both mechanisms consistently outperformed non-targeted baselines while preserving data confidentiality and integrity. This research establishes targeted transfer learning as a practical framework that enables machine learning models to adapt reliably, securely, and efficiently across image classification, network security, and distributed collaborative environments.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/e3fd-gd70

ISBN

9798173767967

ORCID

0000-0001-6270-4677

Download

Share

COinS