论文标题
通过双层优化对自适应加权节点的联合学习
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization
论文作者
论文摘要
我们提出了一种使用加权节点的联合学习方法，可以在其中修改权重以在单独的验证集上优化模型的性能。该问题被表述为双重优化，其中内部问题是带有加权节点的联合学习问题，外部问题着重于基于从内部问题返回的模型的验证性能来优化权重。沟通效率的联合优化算法旨在解决此双重优化问题。在遇到错误的假设下，我们分析了输出模型的概括性能，并确定我们的方法在理论上仅优于训练模型，而仅在本地训练和使用静态且均匀分布的权重进行联合学习。
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.
