<p>Wireless Sensor Networks (WSN) adhere to energy dependency and resource constraints due to which energy scavenging or harvesting draws a vital focus. For sustainable communication/data transmission/real-time application support, the lifetime and remaining battery energy of the sensor nodes are to be preserved or cultivated. For augmenting this feature, this article proposes a Graded Allocation-fused Energy Scavenging Method (GA-ESM). The proposed method identifies the node schedules based on their remaining lifetime for decision-making. The decisions on energy scavenging or allocation sharing are performed using a federated learning paradigm. First, the available schedules for a node are computed and the energy sufficiency for the cumulative schedules is pre-estimated. The overleaping schedules compared to the lifetime are graded based on their maximum time-out and are allocated to high energy nodes. Concurrently the nodes that are disconnected from the schedules are induced for energy scavenging through the conventional piezoelectric or photovoltaic harvesting methods. Federated learning recurrently performs two classifications: overleaping schedules and node lifetime. The grades are provided based on fewer schedules and a sustainable lifetime exhibited by the nodes. The proposed GA-ESM improves scavenging rate by 11.37%, retains lifetime by 8.83%, and schedule allocation by 9.39%. This method reduces node failures by 10.63%, delay by 9.61%, and overlapping schedules by 11.1% for the maximum schedules.</p>