3 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems
Tianyu Zhao, Mojtaba Taherisadr, Salma Elmalaki
Achieving fairness in sequential-decision making systems within Human-in-the-Loop (HITL) environments is a critical concern, especially when multiple humans with different behavior…