Communication Trade-offs in Federated Learning of Spiking Neural Networks

Autor:	Chaki, Soumi, Weinberg, David, Özcelikkale, Ayca
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in the training of SNNs in a distributed scheme are not well understood. Here, we consider SNNs in a federated learning setting where a high-quality global model is created by aggregating multiple local models from the clients without sharing any data. We investigate federated learning for training multiple SNNs at clients when two mechanisms reduce the uplink communication cost: i) random masking of the model updates sent from the clients to the server; and ii) client dropouts where some clients do not send their updates to the server. We evaluated the performance of the SNNs using a subset of the Spiking Heidelberg digits (SHD) dataset. The results show that a trade-off between the random masking and the client drop probabilities is crucial to obtain a satisfactory performance for a fixed number of clients. Comment: 5 pages, 7 figures, 1 algorithm, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.00928 Zobrazit plný text záznamu View this record from Arxiv