Albert Y. Zomaya
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FEDERATED LARGE LANGUAGE MODELS IN HEALTHCARE Open
The convergence of Federated Learning (FL) and Large Language Models (LLMs) represents a transformative opportunity in healthcare. FL allows decentralized model training across multiple institutions without sharing sensitive data, which is…
View article: Agentic Services Computing
Agentic Services Computing Open
The rise of large language model (LLM)-powered agents is transforming services computing, moving it beyond static, request-driven functions toward dynamic, goal-oriented, and socially embedded multi-agent ecosystems. We propose Agentic Ser…
Multi-Agent and Group Aware Reasoning for Robust Traffic Signal Control Through Jacobian Norm Based on Adversarial Defense Open
Traffic Signal Control (TSC) is a critical component of intelligent transportation systems, directly influencing urban mobility, congestion levels, and environmental sustainability. While Multi-Agent Reinforcement Learning (MARL) has shown…
View article: Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning
Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning Open
Federated Learning (FL) enables collaborative model training across decentralized data sources while preserving data privacy. However, the growing size of Machine Learning (ML) models poses communication and computation challenges in FL. L…
View article: Edge Intelligence with Spiking Neural Networks
Edge Intelligence with Spiking Neural Networks Open
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditional deep learning models require significant computational …
Perspectives on Managing AI Ethics in the Digital Age Open
The rapid advancement of artificial intelligence (AI) has introduced unprecedented opportunities and challenges, necessitating a robust ethical and regulatory framework to guide its development. This study reviews key ethical concerns such…
Falcon: Advancing Asynchronous BFT Consensus for Lower Latency and Enhanced Throughput Open
Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is designed by executing sequential instances of the Asynchronous C…
NaFV-Net: An Adversarial Four-view Network for Mammogram Classification Open
Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has becom…
View article: High-Fidelity EEG Generation: Generative Adversarial Network Highlighting Time-Frequency-Spatial Features Regulated by Global Dynamics Supervision
High-Fidelity EEG Generation: Generative Adversarial Network Highlighting Time-Frequency-Spatial Features Regulated by Global Dynamics Supervision Open
Electroencephalogram (EEG) analysis has heavily relied on sophisticated machine learning methods. However, the limited availability of diverse and extensive EEG datasets often underscores the need for reliable data augmentation approaches.…
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning Open
As data volumes expand rapidly, distributed machine learning has become essential for addressing the growing computational demands of modern AI systems. However, training models in distributed environments is challenging with participants …
View article: Quick-MIMIC: A Multimodal Data Extraction Pipeline for MIMIC with Parallelization
Quick-MIMIC: A Multimodal Data Extraction Pipeline for MIMIC with Parallelization Open
Medical big data with artificial intelligence are vital in advancing digital medicine. However, the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle…
Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework Open
This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in th…
Harnessing Federated Learning for Digital Forensics in IoT: A Survey and Introduction to the IoT-LF Framework Open
The proliferation of the Internet of Things (IoT) systems has fueled a surge in cybercrime, particularly through advanced persistent threats, such as botnets and ransomware, posing challenges for centralized Digital Forensics (DF) solution…
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks Open
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL framework…
View article: Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing
Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing Open
Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to re…
ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks Open
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating mod…
Online demand peak shaving with machine‐learned advice in digital twins Open
As the use of physical instruments grows, control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology. Demand load management is central to energy systems within digital twins, w…
Analysis of DNS Dependencies and their Security Implications in Australia: A Comparative Study of General and Indigenous Populations Open
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We analyze the DNS dependencies of Australian government doma…
RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS Open
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated …
Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Models Open
Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (F…
GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard Mechanism Open
Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named…
View article: Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles
Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles Open
Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safe…
Boosting Communication Efficiency of Federated Learning's Secure Aggregation Open
Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, whe…
CGS-Mask: Making Time Series Predictions Intuitive for All Open
Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evalu…
Unraveling Pain Levels: A Data-Uncertainty Guided Approach for Effective Pain Assessment Open
Pain, a primary reason for seeking medical help, requires essential pain assessment for effective management. Studies have recognized electrodermal activity (EDA) signaling's potential for automated pain assessment, but traditional algorit…
Game Theory Based Optimal Defensive Resources Allocation with Incomplete Information in Cyber-Physical Power Systems Against False Data Injection Attacks Open
Modern power grid is fast emerging as a complex cyber-physical power system (CPPS) integrating physical current-carrying components and processes with cyber-embedded computing, which faces increasing cyberspace security threats and risks. …
Poison-Tolerant Collaborative Filtering Against Poisoning Attacks on Recommender Systems Open
Personalized recommendation is deemed ubiquitous. Indeed, it has been applied to several online services (e.g., E-commerce, advertising, and social media applications, to name a few). Learning unknown user preferences from user-provided da…