1 citations · 1 across the 6 of their papers we have counts for
7 papers
MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learning
Akshay Mhatre, Vikram Karthick, Deepti Gupta +1
Federated Learning (FL) enables distributed model training without centralizing data but suffers from high communication overhead. To address this, we propose Block-Wise Marginal U…
From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
Hassan Karim, Sai Sitharaman, Deepti Gupta +1
Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms…
LLM-HPC++: Evaluating LLM-Generated Modern C++ and MPI+OpenMP Codes for Scalable Mandelbrot Set Computation
Patrick Diehl, Noujoud Nader, Deepti Gupta
Parallel programming remains one of the most challenging aspects of High-Performance Computing (HPC), requiring deep knowledge of synchronization, communication, and memory models.…
Scalable Privilege Analysis for Multi-Cloud Big Data Platforms: A Hypergraph Approach
Sai Sitharaman, Hassan Karim, Deepti Gupta +1
The rapid adoption of multi-cloud environments has amplified risks associated with privileged access mismanagement. Traditional Privileged Access Management (PAM) solutions based o…
Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
Akshay Mhatre, Noujoud Nader, Patrick Diehl +1
Large Language Models (LLMs) such as ChatGPT-4, Claude 3, and LLaMA 4 are increasingly embedded in software/application development, supporting tasks from code generation to debugg…
AndroIDS : Android-based Intrusion Detection System using Federated Learning
Akarsh K Nair, Shanik Hubert Satheesh Kumar., Deepti Gupta
The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other c…
SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Likhitha Annapurna Kavuri, Akshay Mhatre, Akarsh K Nair +1
Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversari…