26 citations · 103 across the 41 of their papers we have counts for
44 papers
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Arathi Nair M, J. Harshan, Anwitaman Datta
In federated learning, mitigating class imbalance is essential to improve minority-class performance. A common approach to address this problem is to augment minority-class samples…
Robust Berrut-Approximated Coded Computing via Discrete Cosine Transforms
Rimpi Borah, J. Harshan
Coded computing is a reliable and fault-tolerant paradigm for executing large-scale computational tasks over distributed worker nodes. Among existing coded computing frameworks, Be…
Cooperative Mitigation against Learning-Based Reactive Jammers: Analysis and SDR Validation
Soumita Hazra, J. Harshan
Motivated by recent developments in full-duplex radios, cognitive radios, and data-driven signal-processing, we propose a novel class of reactive jamming adversaries wherein the ad…
Balancing Privacy and Robustness in Coded Computing Under Profiled Workers
Rimpi Borah, J. Harshan, Aaditya Sharma
In distributed computing with untrusted workers, the assignment of evaluation indices plays a critical role in determining both privacy and robustness. In this work, we study how t…
Basis-Spline Assisted Coded Computing: Strategies and Error Bounds
Rimpi Borah, J. Harshan, V. Lalitha
Coded computing has emerged as a key framework for addressing the impact of stragglers in distributed computation. While polynomial functions often admit exact recovery under exist…
Robust Analog Lagrange Coded Computing: Theory and Algorithms via Discrete Fourier Transforms
Rimpi Borah, J. Harshan
Analog Lagrange Coded Computing (ALCC) is a recently proposed computational paradigm wherein certain computations over analog datasets are efficiently performed using distributed w…