most citedFairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

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…

cs.LG2025★ 1 cited

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…

cs.LG2025★ 2 cited

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…

cs.CL2025★ 1 cited

Towards Adaptive Context Management for Intelligent Conversational Question Answering

Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake +1

This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework i…

cs.CL2025

A Survey of the State-of-the-Art in Conversational Question Answering Systems

Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake +3

Conversational Question Answering (ConvQA) systems have emerged as a pivotal area within Natural Language Processing (NLP) by driving advancements that enable machines to engage in…