6 papers
Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem
Nazreen Shah, Naveen Kumar Reddy Somireddy, Zubair Shaban +2
Federated Continual Learning (FCL) is fundamental to real-world distributed learning systems, requiring models to adapt to sequential, non-IID data across clients while mitigating…
Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks
Zubaida Fatima, Zubair Shaban, Yusuf Jamal +3
The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in…
Theoretical Foundations of Continual Learning via Drift-Plus-Penalty
Nazreen Shah, Govinda Arya, Bharath B. N. +1
In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch. Continual lea…
Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations
Sumedha Chugh, Ranjitha Prasad, Nazreen Shah
Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic expl…
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad
The holy grail of machine learning is to enable Continual Federated Learning (CFL) to enhance the efficiency, privacy, and scalability of AI systems while learning from streaming d…
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks
Zubair Shaban, Nazreen Shah, Ranjitha Prasad
In 6G wireless networks, Artificial Intelligence (AI)-driven applications demand the adoption of Federated Learning (FL) to enable efficient and privacy-preserving model training a…