Over-the-Air Federated Learning from Heterogeneous Data
- URL: http://arxiv.org/abs/2009.12787v2
- Date: Fri, 2 Oct 2020 06:37:08 GMT
- Title: Over-the-Air Federated Learning from Heterogeneous Data
- Authors: Tomer Sery, Nir Shlezinger, Kobi Cohen and Yonina C. Eldar
- Abstract summary: Federated learning (FL) is a framework for distributed learning of centralized models.
We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local gradient descent (SGD) FL algorithm.
We numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
- Score: 107.05618009955094
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning (FL) is a framework for distributed learning of
centralized models. In FL, a set of edge devices train a model using their
local data, while repeatedly exchanging their trained updates with a central
server. This procedure allows tuning a centralized model in a distributed
fashion without having the users share their possibly private data. In this
paper, we focus on over-the-air (OTA) FL, which has been suggested recently to
reduce the communication overhead of FL due to the repeated transmissions of
the model updates by a large number of users over the wireless channel. In OTA
FL, all users simultaneously transmit their updates as analog signals over a
multiple access channel, and the server receives a superposition of the analog
transmitted signals. However, this approach results in the channel noise
directly affecting the optimization procedure, which may degrade the accuracy
of the trained model. We develop a Convergent OTA FL (COTAF) algorithm which
enhances the common local stochastic gradient descent (SGD) FL algorithm,
introducing precoding at the users and scaling at the server, which gradually
mitigates the effect of the noise. We analyze the convergence of COTAF to the
loss minimizing model and quantify the effect of a statistically heterogeneous
setup, i.e. when the training data of each user obeys a different distribution.
Our analysis reveals the ability of COTAF to achieve a convergence rate similar
to that achievable over error-free channels. Our simulations demonstrate the
improved convergence of COTAF over vanilla OTA local SGD for training using
non-synthetic datasets. Furthermore, we numerically show that the precoding
induced by COTAF notably improves the convergence rate and the accuracy of
models trained via OTA FL.
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