论文标题
通过固有可塑性对储层的联合适应
Federated Adaptation of Reservoirs via Intrinsic Plasticity
论文作者
论文摘要
我们提出了一种新颖的算法，用于在客户服务器方案中使用Echo State Networks（ESN）进行联合学习。特别是，我们的提议着重于通过将内在的可塑性与联邦平均结合结合的储层适应。前者是一种基于梯度的方法，用于以本地和无监督的方式适应水库的非线性，而后者为在联合场景中学习提供了学习的框架。与先前对文献中现有的联合ESN的方法相比，我们从人类监测中评估了对现实世界数据集的方法。结果表明，将储层与我们的算法相适应，可对全球模型的性能有重大改进。
We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario. In particular, our proposal focuses on the adaptation of reservoirs by combining Intrinsic Plasticity with Federated Averaging. The former is a gradient-based method for adapting the reservoir's non-linearity in a local and unsupervised manner, while the latter provides the framework for learning in the federated scenario. We evaluate our approach on real-world datasets from human monitoring, in comparison with the previous approach for federated ESNs existing in literature. Results show that adapting the reservoir with our algorithm provides a significant improvement on the performance of the global model.
