1 citations · 2 across the 9 of their papers we have counts for
10 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…
Nishpaksh: TEC Standard-Compliant Framework for Fairness Auditing and Certification of AI Models
Shashank Prakash, Ranjitha Prasad, Avinash Agarwal
The growing reliance on Artificial Intelligence (AI) models in high-stakes decision-making systems, particularly within emerging telecom and 6G applications, underscores the urgent…
Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
Praharsh Nanavati, Ranjitha Prasad, Karthikeyan Shanmugam
Estimating treatment effects from observational data is challenging due to two main reasons: (a) hidden confounding, and (b) covariate mismatch (control and treatment groups not ha…
Unified Robust and Convergent Over-The-Air Federated Learning under Channel Noise and Heterogeneity
Zubair Shaban, Nazreen Shah, Ranjitha Prasad
Federated learning (FL) has emerged as a key enabler of distributed model training across ubiquitous wireless edge devices while preserving data privacy in next-generation wireless…
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…
On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning
Arpit Guleria, J. Harshan, Ranjitha Prasad +1
Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets can efficiently address both the…
Federated Learning for Wireless Applications: A Prototype
Varun Laxman Muttepawar, Arjun Mehra, Zubair Shaban +2
Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather immense data via onboard sensors and mobile applications. This offers an amazing opportuni…