5 citations · 5 across the 5 of their papers we have counts for
6 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…
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…
Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
Obaidullah Zaland, Zulfiqar Ahmad Khan, Monowar Bhuyan
Modern big-data systems generate massive, heterogeneous, and geographically dispersed streams that are large-scale and privacy-sensitive, making centralization challenging. While f…
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…
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…