1 citations · 1 across the 9 of their papers we have counts for
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GCA: Global Centroid Alignment in Federated Learning
Jong-Ik Park, Harry Jiang, Logan Blakely +3
Autoencoder (AE)-based federated learning (FL) is attractive for anomaly detection when clients have limited local data. However, conventional FL exchanges AE parameters or gradien…
FedLNS: Leverage LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLMs
Kai Li, Jong-Ik Park, Carlee Joe-Wong +2
Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or optimization process that produces…
Representation Matters in Randomized Smoothing for Audio Classification
Jong-Ik Park, Shreyas Chaudhari, José M. F. Moura +1
Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added. In audio classification, this space is often not uniquely defined as standard pipe…
RRISE: Robust Radius Inference via a Surrogate Estimator
Jong-Ik Park, Shreyas Chaudhari, Carlee Joe-Wong +1
Randomized smoothing (RS) uses a smoothed classifier to provide architecture-agnostic certificates of classification robustness, but its dependence on per-input Monte Carl…
GLUE: Gradient-free Learning to Unify Experts
Jong-Ik Park, Shreyas Chaudhari, Srinivasa Pranav +2
In many deployed systems (multilingual ASR, cross-hospital imaging, region-specific perception), multiple pretrained specialist models coexist. Yet, new target domains often requir…