7 papers
Hierarchical Adaptive Consensus Network: A Dynamic Framework for Scalable Consensus in Collaborative Multi-Agent AI Systems
Rathin Chandra Shit, Sharmila Subudhi
The consensus strategies used in collaborative multi-agent systems (MAS) face notable challenges related to adaptability, scalability, and convergence certainties. These approaches…
Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks
Rathin Chandra Shit, Sharmila Subudhi
The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication…
Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization
Rathin Chandra Shit, Sharmila Subudhi
The optimization of urban traffic is threatened by the complexity of achieving a balance between transport efficiency and the maintenance of privacy, as well as the equitable distr…
Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance
Rathin Chandra Shit, Sharmila Subudhi
The real-time performance, adversarial resiliency, and privacy preservation are the most important metrics that need to be balanced to practice collision avoidance in large-scale m…
Path-Coordinated Continual Learning with Neural Tangent Kernel-Justified Plasticity: A Theoretical Framework with Near State-of-the-Art Performance
Rathin Chandra Shit
Catastrophic forgetting is one of the fundamental issues of continual learning because neural networks forget the tasks learned previously when trained on new tasks. The proposed f…
Multi-Robot Task Allocation for Homogeneous Tasks with Collision Avoidance via Spatial Clustering
Rathin Chandra Shit, Sharmila Subudhi
In this paper, a novel framework is presented that achieves a combined solution based on Multi-Robot Task Allocation (MRTA) and collision avoidance with respect to homogeneous meas…