12 citations · 15 across the 5 of their papers we have counts for
5 papers
Bit-ViP: Leveraging Bit-planes to Preserve Visual Privacy in Images through Obfuscation
Vishesh Kumar Tanwar, Ashish Gupta, Sanjay Madria +1
The unprecedented growth of computer vision applications, such as surveillance systems and social media, raises security and visual privacy concerns, especially when data is stored…
Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
Aakar Mathur, Mohammed Ruknuddin, Ashish Gupta
Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surface from variational circuit t…
Energy-Efficient Split Learning for Resource-Constrained Environments: A Smart Farming Solution
Keiwan Soltani, Vishesh Kumar Tanwar, Ashish Gupta +1
Smart farming systems encounter significant challenges, including limited resources, the need for data privacy, and poor connectivity in rural areas. To address these issues, we pr…
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Aakar Mathur, Ashish Gupta, Sajal K. Das
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learnin…
Tackling Selfish Clients in Federated Learning
Andrea Augello, Ashish Gupta, Giuseppe Lo Re +1
Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL i…