Communication-efficient personalization in federated learning for edge devices

This dissertation advances practical, privacy-preserving federated learning under real-world constraints of heterogeneity, limited resources, and diverse data modalities. It develops algorithms that make collaborative training more efficient, robust, and personalized without centralizing data. First, we introduce a dataset-aware dynamic pruning strategy coupled with gradient control to curb overfitting on heterogeneous clients, stabilize convergence, and lower both computation and communication during local updates. Next, we propose a multimodal federated framework with dual adapters: one larger adapter that is private to each client for personalization and a compact, shared adapter for knowledge transfer, augmented with selective pruning to balance local adaptation and global generalization for vision and language tasks. Then, we present a lightweight, convolution-based approach to time-series forecasting that pairs learnable trend/seasonality decomposition with an efficient federated protocol, enabling accurate prediction across distributed, streaming signals on constrained devices. Finally, we develop adaptive federated distillation with dual adapters and instance-wise fusion, aligning shared knowledge at the server while preserving client-specific representations to improve personalization under non-IID data. Together, these contributions chart a cohesive path toward scalable, resource-aware, and personalization friendly federated learning across data types and tasks, closing the gap between theoretical promise and deployment reality while maintaining user privacy.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

dissertation

Discipline thesis:degree_discipline

Engineering

Department dc:contributor.department

Department of Computer Science

Grantor

Iowa State University

Year dc:date.issued

2025

Author dc:creator

• Nguyen, Duy Phuong

Advisors dc:contributor.advisor

• Jannesari, Ali
• Basu, Samik
• Huang, Xiaoqiu
• Zhang, Wensheng
• Gao, Hongyang

Language dc:language.iso

en

DOI

https://doi.org/10.31274/td-20260223-50

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/1wgeg5lr