Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions

ArXi:2605.15355v1 Announce Type: new Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, making them attractive for resource-constrained edge devices. Federated learning enables such devices to train collaboratively without sharing raw data. In time-series applications, edge devices often collect data at different time resolutions due to hardware and energy constraints.