Author

Taehong Kim

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#federated learning Nov 2026

FedIPSA: Personalized Federated Learning via Importance-Based Parameter Masking and Selective Alignment

Federated learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it well-suited for privacy-sensitive Internet of Things (IoT) environments. However, under heterogeneous [nonindependent and identically distributed (IID)] data distributions, classical FL often suffers...

Anh-Kiet Vo, Thien Huynh-The, Taehong Kim · IEEE Internet of Things Jour... · 0 citations

Open access Aug 2026

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

FedA2L is introduced, a method that dynamically adjusts layer-wise LRs based on model divergence signals that achieves up to 4.94 times faster convergence than vanilla DFL and reduces communication rounds by up to 59% compared to scheduler-based baselines.

V. T. Vo, K. Nguyen, Taehong Kim · Future generations computer... · 0 citations