4 citations · 6 across the 22 of their papers we have counts for
25 papers
Cross-Facility LLM Pre-training on HPC: Elastic Aggregation, Data Leasing, and Queue-Aware Placement
Zarè Palanciyan, Thomas van Osch, Douwe van der Wal +2
Academic compute is fragmented: allocations are granted per facility, and facilities differ in accelerators and software stacks, schedule jobs independently, and share neither a ne…
From Pilots to Production: Lessons in Cross-Institutional Federated Training and Artificial Intelligence for Science
Olivera Kotevska, Max Carlson, Yan Gao +10
Many of the most valuable scientific datasets cannot be centralized: they are proprietary, export-controlled, classified, or bound by data-sovereignty restrictions. This inverts th…
Privacy Foundations for Multi-Institutional Scientific Artificial Intelligence
Olivera Kotevska, Sumit Jha, Aur'elien Bellet +6
Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, univ…
Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs
Ravi Ranjan, Olivera Kotevska, Agoritsa Polyzou
Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machin…
Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers
Olivera Kotevska, Ian Goethert, Michael McGee +11
Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage malignancies can be subtle o…
Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning
Abhijith Babu, Ramneet Kaur, Vishal Pramanik +7
Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particularly in hidden-profile setting…
Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics
Mohamed Akrout, Olivera Kotevska, Dan Wilson
Large Language Models (LLMs) are increasingly deployed in high-stakes applications, yet their tendency to generate toxic, harmful, or policy-violating content poses significant ris…
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets () according to institutional policies and da…
Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models
Zikai Zhang, Rui Hu, Olivera Kotevska +1
Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate…
Closed-Loop Neural Activation Control in Vision-Language-Action Models
Abhijith Babu, Ramneet Kaur, Nathaniel D. Bastian +5
Vision-Language-Action (VLA) models can be steered at test time by intervening on semantically meaningful internal directions, but existing methods use a fixed steering coefficient…
SelfGrader: LLM Jailbreak Detection via Anchored Token-Level Logits
Zikai Zhang, Rui Hu, Olivera Kotevska +1
Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing guardrail methods typically rely on int…
Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
Zhishuai Guo, Wenhan Wu, Chen Chen +3
Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to priva…
XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts
Jiahao Xu, Rui Hu, Olivera Kotevska +1
Multi-bit watermarking has emerged as a promising solution for embedding imperceptible binary messages into Large Language Model (LLM)-generated text, enabling reliable attribution…
Automated Membership Inference Attacks (AutoMIA): Discovering MIA Signal Computations using LLM Agents
Toan Tran, Olivera Kotevska, Li Xiong
Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have emerged as an important framew…
Accuracy is Not Enough: Poisoning Interpretability in Federated Learning via Color Skew
Farhin Farhad Riya, Shahinul Hoque, Jinyuan Stella Sun +1
As machine learning models are increasingly deployed in safety-critical domains, visual explanation techniques have become essential tools for supporting transparency. In this work…
HEMERA: A Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data
Maria Mahbub, Robert J. Klein, Myvizhi Esai Selvan +12
Lung cancer (LC) is the third most common cancer and the leading cause of cancer deaths in the US. Although smoking is the primary risk factor, the occurrence of LC in never-smoker…
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
Sahil Tyagi, Andrei Cozma, Olivera Kotevska +1
Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework desi…
Majority Bit-Aware Watermarking For Large Language Models
Jiahao Xu, Rui Hu, Olivera Kotevska +1
The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To address this issue, watermarking method…
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
Jiahao Xu, Rui Hu, Olivera Kotevska
Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. Howe…
Traceable Black-box Watermarks for Federated Learning
Jiahao Xu, Rui Hu, Olivera Kotevska +1
Due to the distributed nature of Federated Learning (FL) systems, each local client has access to the global model, which poses a critical risk of model leakage. Existing works hav…
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Joel Brogan, Olivera Kotevska, Anibely Torres +2
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in t…
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
Yichuan Shi, Olivera Kotevska, Viktor Reshniak +2
Federated Learning (FL) has emerged as a leading paradigm for decentralized, privacy preserving machine learning training. However, recent research on gradient inversion attacks (G…
Whitepaper on Reusable Hybrid and Multi-Cloud Analytics Service Framework
Gregor von Laszewski, Wo Chang, Russell Reinsch +5
Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isol…
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
Yi Zhang, Yuying Zhao, Zhaoqing Li +5
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many o…