5 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
Sindhuja Madabushi, Haider Ali, Ahmad Faraz Khan +5
Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential,…
Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering
Anas Mohamed, Azal Ahmad Khan, Xinran Wang +5
Generative AI can now synthesize strikingly realistic images from text, yet output quality remains highly sensitive to how prompts are phrased. Direct Preference Optimization (DPO)…
OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning
Sindhuja Madabushi, Ahmad Faraz Khan, Haider Ali +1
Vertical Federated Learning (VFL) enables organizations with disjoint feature spaces but shared user bases to collaboratively train models without sharing raw data. However, existi…
FLStore: Efficient Federated Learning Storage for non-training workloads
Ahmad Faraz Khan, Samuel Fountain, Ahmed M. Abdelmoniem +2
Federated Learning (FL) is an approach for privacy-preserving Machine Learning (ML), enabling model training across multiple clients without centralized data collection. With an ag…
LADs: Leveraging LLMs for AI-Driven DevOps
Ahmad Faraz Khan, Azal Ahmad Khan, Anas Mohamed +7
Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions la…