论文标题
使用神经形态处理器的一杆联合学习
One-Shot Federated Learning with Neuromorphic Processors
论文作者
论文摘要
作为非常低的功率，在移动设备中使用神经形态处理器来解决机器学习问题是传统冯·诺伊曼处理器的有前途替代方法。联合学习使诸如移动设备之类的实体可以协作学习共享模型，同时保持其培训数据本地。此外，联合学习是一种安全的学习方式，因为只有模型之间只需要在模型之间共享模型权重，从而使数据保持私密。在这里，我们证明了联合学习在神经形态处理器中的功效。神经形态处理器受益于协作学习，从而在保留本地数据隐私的同时，在单个处理器模型的一次性学习手势识别任务上实现了最先准确的手势识别任务。
Being very low power, the use of neuromorphic processors in mobile devices to solve machine learning problems is a promising alternative to traditional Von Neumann processors. Federated Learning enables entities such as mobile devices to collaboratively learn a shared model while keeping their training data local. Additionally, federated learning is a secure way of learning because only the model weights need to be shared between models, keeping the data private. Here we demonstrate the efficacy of federated learning in neuromorphic processors. Neuromorphic processors benefit from the collaborative learning, achieving state of the art accuracy on a one-shot learning gesture recognition task across individual processor models while preserving local data privacy.
