论文标题
FEDHEN：异质网络中的联合学习
FedHeN: Federated Learning in Heterogeneous Networks
论文作者
论文摘要
我们为通过异质网络提供了一种新颖的培训配方，用于联合学习，每个设备都可以具有不同的架构。我们介绍了培训，并以较高复杂性设备为侧面目标，以在联合环境中共同培训不同的体系结构。我们从经验上表明，与最先进的方法相比，我们的方法提高了不同架构的性能，并导致沟通节省很高。
We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objective to the devices of higher complexities to jointly train different architectures in a federated setting. We empirically show that our approach improves the performance of different architectures and leads to high communication savings compared to the state-of-the-art methods.
