Modular Federated Learning for Non-IID Data
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Date
2025-06
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Addis Ababa University
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.
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Addis Ababa University