1 citations · 1 across the 6 of their papers we have counts for
6 papers
ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning
Karan Sharma, Aditya Tripathi, Rahul Mishra +1
Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, identical architectures, and cen…
Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs
Dinesh Srivasthav P, Ashok Urlana, Rahul Mishra +2
Machine unlearning aims to selectively remove the influence of specific training samples to satisfy privacy regulations such as the GDPR's 'Right to be Forgotten'. However, many ex…
SpectralKrum: A Spectral-Geometric Defense Against Byzantine Attacks in Federated Learning
Aditya Tripathi, Karan Sharma, Rahul Mishra +1
Federated Learning (FL) distributes model training across clients who retain their data locally, but this architecture exposes a fundamental vulnerability: Byzantine clients can in…
SceneGraMMi: Scene Graph-boosted Hybrid-fusion for Multi-Modal Misinformation Veracity Prediction
Swarang Joshi, Siddharth Mavani, Joel Alex +3
Misinformation undermines individual knowledge and affects broader societal narratives. Despite growing interest in the research community in multi-modal misinformation detection,…
Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with Hyperbolic Graph Neural Networks
Debjyoti Mondal, Rahul Mishra, Chandan Pandey
Image analysis in the euclidean space through linear hyperspaces is well studied. However, in the quest for more effective image representations, we turn to hyperbolic manifolds. T…
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
Yousef Alsenani, Rahul Mishra, Khaled R. Ahmed +1
In recent years, federated learning (FL) has emerged as a promising technique for training machine learning models in a decentralized manner while also preserving data privacy. The…