Federated machine learning enables model training across multiple clients while maintaining data privacy. Vertical Federated Learning (VFL) specifically deals with instances where the clients have different feature sets of the same samples. As federated learning models aim to improve efficiency and adaptability, innovative neural network architectures like Spiking Neural Networks (SNNs) are being leveraged to enable fast and accurate processing at the edge. SNNs, known for their efficiency over Artificial Neural Networks (ANNs), have not been analyzed for their applicability in VFL, thus far. In this paper, we investigate the benefits and trade-offs of using SNN models in a vertical federated learning setting. We implement two different federated learning architectures -- with model splitting and without model splitting -- that have different privacy and performance implications. We evaluate the setup using CIFAR-10 and CIFAR-100 benchmark datasets along with SNN implementations of VGG9 and ResNET classification models. Comparative evaluations demonstrate that the accuracy of SNN models is comparable to that of traditional ANNs for VFL applications, albeit significantly more energy efficient.
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 sen…
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology
Intel Labs（英特尔实验室）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Southern California（南加州大学）
Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Future Network of Intelligence Institute, Chinese University of Hong Kong（香港中文大学（深圳）未来智联网络研究院）
Spiking neural networks (SNNs) recently gained momentum due to their low-power multiplication-free computing and the closer resemblance of biological processes in the nervous system of humans. However, SNNs require very long spike trains (up to 1000) to reach an accuracy similar to their artificial neural network (ANN) counterparts for large models, which offsets efficiency and inhibits its application to low-power systems for real-world use cases. To alleviate this problem, emerging neural encoding schemes are proposed to shorten the spike train while maintaining the high accuracy. However, c…
Evaluation of spiking neural networks requires fetching a large number of synaptic weights to update postsynaptic neurons. This limits parallelism and becomes a bottleneck for hardware. We present an approach for spike propagation based on a probabilistic interpretation of weights, thus reducing memory accesses and updates. We study the effects of introducing randomness into the spike processing, and show on benchmark networks that this can be done with minimal impact on the recognition accuracy. We present an architecture and the trade-offs in accuracy on fully connected and convolutional net…
University of Minnesota（明尼苏达大学）；University of Surrey（萨里大学）；University of Applied Sciences, Stralsund（施特拉尔松德应用科学大学）
In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal that mimics backpropagation but is far less computationally intensive. The weight update calculation at each layer requires only local data apart from this signal. We also employ a rule capable of producing specific output spike trains; by setting the target spike time equal to the actual spike time with a slight negative offset for key high-value neurons the actual spike time becomes as early as possible. In simulated …
Vibe2Spike: Batteryless Wireless Tags for Vibration Sensing with Event Cameras and Spiking Networks
Department of EECS, The University of Tennessee, Knoxville（电子工程与计算机科学系，田纳西大学）；Department of ECE, Oklahoma State University（电气与计算机工程系，俄克拉荷马州立大学）；Accenture Labs, Accenture（Accenture实验室，Accenture）
Vibe2Spike 提出一种无电池无线振动感知框架，利用压电片、齐纳二极管和 LED 组成的超低成本标签采集振动能量并发射可见光脉冲，结合事件相机与演化优化的脉冲神经网络实现活动识别，在五类设备上达到 94.9% 的平均分类适应度。
The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) are run on neuromorphic processors, is regarded as an energy-efficient and robust alternative to the state-of-the-art real-time robotic controllers for low dimensional control tasks. The challenge now for this new computing paradigm is to scale so that it can keep up with real-wo…
Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs
FSTA-SNN:Frequency-based Spatial-Temporal Attention Module for Spiking Neural Networks
Zhejiang University-University of Illinois Urbana Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校研究院）；The College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting
Université de Lille（里尔大学）；Université de Sherbrooke（谢布鲁克大学）；CNRS（法国国家科学研究中心）；INSA Lyon（里昂国立应用科学学院）；Université Grenoble Alpes（格勒诺布尔-阿尔卑斯大学）；Ecole Centrale de Lyon（里昂中央理工学院）
Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timing-dependent plasticity (STDP) mechanism for learning. Existing hardware implementations of SNN are limited in scale or do not have in-hardware learning capability. In this work, we propose a low-cost scalable Network-on-Chip (NoC) based SNN hardware architecture with fully distributed in-hardware STDP learning capability. All hardware neurons work in parallel and comm…
