Robust Federated Learning via Over-The-Air Computation
- URL: http://arxiv.org/abs/2111.01221v1
- Date: Mon, 1 Nov 2021 19:21:21 GMT
- Title: Robust Federated Learning via Over-The-Air Computation
- Authors: Houssem Sifaou and Geoffrey Ye Li
- Abstract summary: Simple averaging of model updates via over-the-air computation makes the learning task vulnerable to random or intended modifications of the local model updates of some malicious clients.
We propose a robust transmission and aggregation framework to such attacks while preserving the benefits of over-the-air computation for federated learning.
- Score: 48.47690125123958
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper investigates the robustness of over-the-air federated learning to
Byzantine attacks. The simple averaging of the model updates via over-the-air
computation makes the learning task vulnerable to random or intended
modifications of the local model updates of some malicious clients. We propose
a robust transmission and aggregation framework to such attacks while
preserving the benefits of over-the-air computation for federated learning. For
the proposed robust federated learning, the participating clients are randomly
divided into groups and a transmission time slot is allocated to each group.
The parameter server aggregates the results of the different groups using a
robust aggregation technique and conveys the result to the clients for another
training round. We also analyze the convergence of the proposed algorithm.
Numerical simulations confirm the robustness of the proposed approach to
Byzantine attacks.
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