5 citations · 7 across the 9 of their papers we have counts for
12 papers
A Collaborative Artificial Intelligence as a Service Composition Dataset
Deepak Kanneganti, Sajib Mistry, Sheik Fattah +1
Artificial Intelligence as a Service (AIaaS) composition is an emerging research area that enables clients to combine multiple AI services to meet complex requirements. A recent ex…
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…
Designing Sustainable Federated Learning as a Service using Neural Architecture Search
Keya Patel, Sajib Mistry, Sheik Fattah +4
The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. These constraints often lead…
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…
pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data
Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Mir Sazzat Hossain +5
Test-time adaptation (TTA) in federated learning (FL) is crucial for handling unseen data distributions across clients, particularly when faced with domain shifts and skewed class…
Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments
Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah +2
The dynamic nature of Internet of Things (IoT) environments challenges the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. The uncertainty and variab…
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
Rakibul Hasan Rajib, Md Akil Raihan Iftee, Mir Sazzat Hossain +4
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL mo…
Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
Minod Perera, Sheik Mohammad Mostakim Fattah, Sajib Mistry +1
Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation c…