Over-The-Air Federated Learning under Byzantine Attacks
- URL: http://arxiv.org/abs/2205.02949v1
- Date: Thu, 5 May 2022 22:09:21 GMT
- Title: Over-The-Air Federated Learning under Byzantine Attacks
- Authors: Houssem Sifaou and Geoffrey Ye Li
- Abstract summary: Federated learning (FL) is a promising solution to enable many AI applications.
FL allows the clients to participate in the training phase, governed by a central server, without sharing their local data.
One of the main challenges of FL is the communication overhead.
We propose a transmission and aggregation framework to reduce the effect of such attacks.
- Score: 43.67333971183711
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated learning (FL) is a promising solution to enable many AI
applications, where sensitive datasets from distributed clients are needed for
collaboratively training a global model. FL allows the clients to participate
in the training phase, governed by a central server, without sharing their
local data. One of the main challenges of FL is the communication overhead,
where the model updates of the participating clients are sent to the central
server at each global training round. Over-the-air computation (AirComp) has
been recently proposed to alleviate the communication bottleneck where the
model updates are sent simultaneously over the multiple-access channel.
However, simple averaging of the model updates via AirComp makes the learning
process vulnerable to random or intended modifications of the local model
updates of some Byzantine clients. In this paper, we propose a transmission and
aggregation framework to reduce the effect of such attacks while preserving the
benefits of AirComp for FL. For the proposed robust approach, the central
server divides the participating clients randomly into groups and allocates a
transmission time slot for each group. The updates of the different groups are
then aggregated using a robust aggregation technique. We extend our approach to
handle the case of non-i.i.d. local data, where a resampling step is added
before robust aggregation. We analyze the convergence of the proposed approach
for both cases of i.i.d. and non-i.i.d. data and demonstrate that the proposed
algorithm converges at a linear rate to a neighborhood of the optimal solution.
Experiments on real datasets are provided to confirm the robustness of the
proposed approach.
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