FedSFA: Federating Spikes Fired, Approximately

Over the last decade rapid advancements have been made in the domain of Artificial Neural Networks. A less known variant of these computing architectures are Spiking Neural Networks, an exceptionally energy efficient approach, which aims to simulate the firing mechanisms of biological neural systems.<br/>Federated Learning is a distributed network training paradigm which has increasingly been gaining popularity, due to its decentralized model training approach, allowing training data to remain in its native device's storage. <br/>The intersection of these two topics has previously been explored, with some works existing on Spiking Neural Networks being trained utilizing Federated Learning. This paper proposes FedSFA, a Federated Spiking Transformer for image classification tasks, accelerated by Spike Firing Approximation Training, attempting to tackle tasks with high degrees of data heterogeneity across participants in a federation. Moreover, a scheduler for decaying the amount of local epochs in each training round is proposed. FedSFA presents similar to marginal improvements on finetuning E-Spikeformer on the CIFAR-10/100 when compared to centralized scenarios. End-to-end training was conducted in SVHN(+1.43\%/5.18\%), and NMNIST(+1.58\%) datasets, while having a smaller resource and temporal footprint.