Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity

• URL: http://arxiv.org/abs/2401.01442v1
• Date: Tue, 2 Jan 2024 21:43:01 GMT
• Title: Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity
• Authors: Seyed Mohammad Azimi-Abarghouyi and Viktoria Fodor
• Abstract summary: We introduce a scalable transmission scheme that efficiently uses a single wireless resource through over-the-air computation. 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.
• Score: 3.8798345704175534
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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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