Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity
CoRR(2024)
摘要
When implementing hierarchical federated learning over wireless networks,
scalability assurance and the ability to handle both interference and device
data heterogeneity are crucial. This work introduces a learning method designed
to address these challenges, along with a scalable transmission scheme that
efficiently uses a single wireless resource through over-the-air computation.
To provide resistance against data heterogeneity, we employ gradient
aggregations. Meanwhile, the impact of interference is minimized through
optimized receiver normalizing factors. For this, we model a multi-cluster
wireless network using stochastic geometry, and characterize the mean squared
error of the aggregation estimations as a function of the network parameters.
We show that despite the interference and the data heterogeneity, the proposed
scheme achieves high learning accuracy and can significantly outperform the
conventional hierarchical algorithm.
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