T. Gadekallu

FedMLKD: Communication-Efficient Federated Learning for UAV Swarm Flash Flood Segmentation

Federated Multi-Level Knowledge Distillation (FedMLKD), a novel framework that enables UAVs to exchange both logits and adaptively pooled intermediate features, is proposed, showing its ability to balance accuracy and efficiency, making it a practical candidate for real-time disaster mapping in bandwidth-constrained UA...

EviSlice: Evidence-Driven and Clinically Aware Zero-Touch Security Slicing for 6G-Enabled Healthcare IoT

Future 6G Healthcare Internet-of-Things (H-IoT) networks must protect clinical services that differ widely in urgency, reliability, and disruption tolerance. Although network slicing and zero-touch management offer a strong foundation for automated healthcare traffic control, existing approaches remain largely infrastr...

PQ-FL-IDS: A Post Quantum Safe Federated Edge Intrusion Detection Scheme underlying 6G

PQ-FL-IDS is presented, a post-quantum (PQ) FL-IDS scheme for 6G edge networks that protects model-update exchange using a real, non-simulated Module-Lattice-Based Key-Encapsulation Mechanism, the Advanced Encryption Standard in Galois/Counter Mode with a 256-bit key, and per-round forward secrecy through ephemeral key...

Agentic AI in Healthcare 5.0: Reference Architecture, Applications, and Challenges

Healthcare systems are increasingly data-rich but remain fragmented, reactive, and difficult to coordinate across institutions, devices, and clinical workflows. Healthcare 5.0 addresses these limitations through human-centricity, resilience, and symbiotic human-artificial intelligence (AI) collaboration. This survey po...

Poster: CMA-FL: Cognitive Multi-Agent Federated Learning for Resource-Aware Drone Communication Attack Detection

CMA-FL is proposed, a cognitive multi-agent assisted FL framework for resource-aware intrusion detection in drone communication networks that achieves 99.6% accuracy with 0.99 precision, recall, and F1-score and reduces response time by more than 80% relative to the evaluated FL, edge-cloud, and cloud-only alternatives...

Chain-of-Thought supervision enables explainable and efficient medical summarization with Small Language Models