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arXiv · 2207.03031

FedHeN: Federated Learning in Heterogeneous Networks

Durmus Alp Emre Acar·Venkatesh Saligrama
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Abstract

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.

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Durmus Alp Emre Acar, Venkatesh Saligrama. 2022-07-07. FedHeN: Federated Learning in Heterogeneous Networks. https://arxiv.org/abs/2207.03031

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