9 papers
When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information
Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam +1
Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks. However, their reliability under uncertainty remains poorl…
AgenticAI-DialogGen: Topic-Guided Conversation Generation for Fine-Tuning and Evaluating Short- and Long-Term Memories of LLMs
Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake +1
Recent advancements in Large Language Models (LLMs) have improved their ability to process extended conversational contexts, yet fine-tuning and evaluating short- and long-term mem…
Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
Noorain Mukhtiar, Adnan Mahmood, Yipeng Zhou +3
Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this s…
CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation
Noorain Mukhtiar, Adnan Mahmood, Quan Z. Sheng
With the proliferation of distributed data sources, Federated Learning (FL) has emerged as a key approach to enable collaborative intelligence through decentralized model training…
FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
Kasun Eranda Wijethilake, Adnan Mahmood, Quan Z. Sheng
Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ide…
FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
Fahmida Islam, Adnan Mahmood, Noorain Mukhtiar +2
Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mit…