FedFall: A Federated Transfer Learning Framework for Privacy-Aware Fall Detection in Elderly Care Using IoT-Edge Devices

1) FedFall framework: a novel architecture that fuses federated and transfer learning to enable privacy-preserving and personalized fall detection.
2) Edge-based deployment strategy: an end-to-end system
that supports real-time inference and training on low-
power IoT devices.
3) Simulation-based validation: demonstration of FedFall’s feasibility using simulated multi-client datasets, high lighting accuracy, latency, and communication efficiency.
4) Adaptation for constrained devices: lightweight models and compression techniques to ensure compatibility with embedded platforms.