TY  - JOUR
AU  - Nielsen, Yuki
AU  - Okafor, Diego
AU  - Wang, Valentina
TI  - Kalman Filtering Applied to Federated Learning
JO  - Open Access Journal of Engineering Applications
PY  - 2026
VL  - 50
IS  - 1
SP  - 954
EP  - 960
AB  - Energy security and decarbonization targets have renewed interest in federated learning as it applies to unmanned aerial vehicles. Numerical predictions from Kalman filtering were compared against a benchmark dataset from unmanned aerial vehicles. Water quality indicators improved by 28% after the recommended measures were implemented. The framework can inform both new construction and the retrofitting of existing assets.
KW  - federated learning
KW  - Kalman filtering
KW  - smart grid
KW  - biodiesel
KW  - arsenic removal
SN  - 3105-7004
DO  - 10.55555/oajea.2026.5001.048
UR  - https://journal.bsaps.com/article/10312
ER  -