12 citations · 17 across the 9 of their papers we have counts for
9 papers
Utility-Driven Spatial Data Sampling for UAV-Assisted Scientific Smart Farming
Keiwan Soltani, Sajal K. Das
Large smart-farming deployments generate continuous scientific data from spatially distributed sensors, including soil, humidity, temperature, crop-health, and pest-related measure…
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…
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
Ferdinand Kahenga, Antoine Bagula, Patrick Sello +1
Federated learning in practice must contend with heterogeneous feature spaces, severe non-IID data, and scarce labels across clients. We present FedFusion, a federated transfer-lea…
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
Ferdinand Kahenga, Antoine Bagula, Sajal K. Das +1
Federated Learning (FL) has emerged as a powerful paradigm for privacy-preserving model training, yet deployments in sensitive domains such as healthcare face persistent challenges…
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…
DATAMUt: Deterministic Algorithms for Time-Delay Attack Detection in Multi-Hop UAV Networks
Keiwan Soltani, Federico Corò, Punyasha Chatterjee +1
Unmanned Aerial Vehicles (UAVs), also known as drones, have gained popularity in various fields such as agriculture, emergency response, and search and rescue operations. UAV netwo…
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…
Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production
Behzad J. Balabaygloo, Barituka Bekee, Samuel W. Blair +18
To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture exper…