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arXiv · 2207.10751

Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization

Yankun Huang·Qihang Lin·Nick Street·Stephen Baek
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Abstract

We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is formulated as a bilevel optimization where the inner problem is a federated learning problem with weighted nodes and the outer problem focuses on optimizing the weights based on the validation performance of the model returned from the inner problem. A communication-efficient federated optimization algorithm is designed to solve this bilevel optimization problem. Under an error-bound assumption, we analyze the generalization performance of the output model and identify scenarios when our method is in theory superior to training a model only locally and to federated learning with static and evenly distributed weights.

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Yankun Huang, Qihang Lin, Nick Street, Stephen Baek. 2022-07-21. Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization. https://arxiv.org/abs/2207.10751

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