10 papers
Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah +3
Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across hea…
OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence
Keya Patel, Sajib Mistry, Sheik Mohammad Mostakim Fattah +1
We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptiv…
Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Obaidullah Zaland, Sajib Mistry, Monowar Bhuyan
Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables dece…
HyPCA-Net: Advancing Multimodal Fusion in Medical Image Analysis
J. Dhar, M. K. Pandey, D. Chakladar +4
Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as skin cancer detection, dementia…
Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Fattah +2
We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and co…
FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization
Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Amin Ahsan Ali +3
Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, exis…