8 papers
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki
Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals dispropor…
PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation
Mahmoud Srewa, Praneetsai Iddamsetty, Mohammad Abdullah Al Faruque +1
Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or…
PACIFIC: Can LLMs Discern the Traits Influencing Your Preferences? Evaluating Personality-Driven Preference Alignment in LLMs
Tianyu Zhao, Siqi Li, Yasser Shoukry +1
User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet how to reliably leverage preference signals for answer generation remains under-expl…
APPA: Adaptive Preference Pluralistic Alignment for Fair Federated RLHF of LLMs
Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki
Aligning large language models (LLMs) with diverse human preferences requires pluralistic alignment, where a single model must respect the values of multiple distinct groups simult…
A Systematic Evaluation of Preference Aggregation in Federated RLHF for Pluralistic Alignment of LLMs
Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki
This paper addresses the challenge of aligning large language models (LLMs) with diverse human preferences within federated learning (FL) environments, where standard methods often…
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning
Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki
Ensuring Large Language Models (LLMs) align with diverse human preferences while preserving privacy and fairness remains a challenge. Existing methods, such as Reinforcement Learni…