On Addressing Heterogeneity in Federated Learning for Autonomous
Vehicles Connected to a Drone Orchestrator
- URL: http://arxiv.org/abs/2108.02712v1
- Date: Thu, 5 Aug 2021 16:25:48 GMT
- Title: On Addressing Heterogeneity in Federated Learning for Autonomous
Vehicles Connected to a Drone Orchestrator
- Authors: Igor Donevski, Jimmy Jessen Nielsen, Petar Popovski,
- Abstract summary: We envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles.
We focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle.
- Score: 32.61132332561498
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we envision a federated learning (FL) scenario in service of
amending the performance of autonomous road vehicles, through a drone traffic
monitor (DTM), that also acts as an orchestrator. Expecting non-IID data
distribution, we focus on the issue of accelerating the learning of a
particular class of critical object (CO), that may harm the nominal operation
of an autonomous vehicle. This can be done through proper allocation of the
wireless resources for addressing learner and data heterogeneity. Thus, we
propose a reactive method for the allocation of wireless resources, that
happens dynamically each FL round, and is based on each learner's contribution
to the general model. In addition to this, we explore the use of static methods
that remain constant across all rounds. Since we expect partial work from each
learner, we use the FedProx FL algorithm, in the task of computer vision. For
testing, we construct a non-IID data distribution of the MNIST and FMNIST
datasets among four types of learners, in scenarios that represent the quickly
changing environment. The results show that proactive measures are effective
and versatile at improving system accuracy, and quickly learning the CO class
when underrepresented in the network. Furthermore, the experiments show a
tradeoff between FedProx intensity and resource allocation efforts.
Nonetheless, a well adjusted FedProx local optimizer allows for an even better
overall accuracy, particularly when using deeper neural network (NN)
implementations.
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