Modular Federated Learning for Non-IID Data
| dc.contributor.advisor | Beakal Gizachew (PhD) | |
| dc.contributor.author | Samuel Hailemariam | |
| dc.date.accessioned | 2025-07-30T17:54:57Z | |
| dc.date.available | 2025-07-30T17:54:57Z | |
| dc.date.issued | 2025-06 | |
| dc.description.abstract | Federated Learning (FL) promises privacy-preserving collaboration across distributed clients but is hampered by three key challenges: severe accuracy degradation under non-IID data, high communication and computational demands on edge devices, and a lack of built-in explainability for debugging, user trust, and regulatory compliance. To bridge this gap, we propose two modular FL pipelines—SPATL-XL and SPATL-XLC—that integrate SHAP-driven pruning with, in the latter, dynamic client clustering. SPATL-XL applies SHAP-based pruning to the largest layers, removing low-impact parameters to both reduce model size and sharpen interpretability, whereas SPATL-XLC further groups clients via lightweight clustering to reduce communication overhead and smooth convergence in low-bandwidth, high-client settings. In experiments on CIFAR-10 and Fashion-MNIT over 200 communication rounds under IID and Dirichlet non-IID splits, our pipelines lower per-round communication to 13.26 MB, speed up end-to-end training by 1.13×, raise explanation fidelity from 30–50% to 89%, match or closely approach SCAFFOLD’s 70.64% top-1 accuracy (SPATL-XL: 70.36%), and maintain stable clustering quality (Silhouette, CHI, DBI) even when only 40–70% of clients participate. These results demonstrate that combining explainability-driven pruning with adaptive clustering yields practical, communication-efficient, and regulation-ready FL pipelines that simultaneously address non-IID bias, resource constraints, and transparency requirements. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5871 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Addis Ababa University | |
| dc.title | Modular Federated Learning for Non-IID Data | |
| dc.type | Thesis |