190 citations · 378 across the 13 of their papers we have counts for
4 papers · 1 filter
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting
Sangamesh Kodge, Gobinda Saha, Kaushik Roy
Machine unlearning is a prominent and challenging field, driven by regulatory demands for user data deletion and heightened privacy awareness. Existing approaches involve retrainin…
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi +1
Decentralized learning enables serverless training of deep neural networks (DNNs) in a distributed manner on multiple nodes. This allows for the use of large datasets, as well as t…
CoDeC: Communication-Efficient Decentralized Continual Learning
Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha +1
Training at the edge utilizes continuously evolving data generated at different locations. Privacy concerns prohibit the co-location of this spatially as well as temporally distrib…
Continual Learning with Scaled Gradient Projection
Gobinda Saha, Kaushik Roy
In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones. This issue…