5 papers
BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage
Akarsh K. Nair, Muhammad Arifur Rahman, David Brown +1
Split learning enables collaborative model training by partitioning neural networks across clients and servers. However, improper split placement can lead to severe privacy leakage…
SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework
Akarsh K Nair, Muhammad Arifur Rahman, Nicholas Shopland +8
Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogene…
AndroIDS : Android-based Intrusion Detection System using Federated Learning
Akarsh K Nair, Shanik Hubert Satheesh Kumar., Deepti Gupta
The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other c…
SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Likhitha Annapurna Kavuri, Akshay Mhatre, Akarsh K Nair +1
Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversari…
A comprehensive analysis of PINNs: Variants, Applications, and Challenges
Afila Ajithkumar Sophiya, Akarsh K Nair, Sepehr Maleki +1
Physics Informed Neural Networks (PINNs) have been emerging as a powerful computational tool for solving differential equations. However, the applicability of these models is still…