27 citations · 47 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
MoCoMR: A Collaborative MR Simulator with Individual Behavior Modeling
Diana Romero, Fatima Anwar, Salma Elmalaki
Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address thi…
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…