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.

Related papers

• Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection [13.825031686864559]
  Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. We present a method to improve convergence in FL by creating a global subset of data on the server and dynamically distributing it across devices. Our approach results in a substantial accuracy boost of approximately 5% for the MNIST dataset, around 18% for CIFAR-10, and 20% for CIFAR-100 with a 10% global subset of data, outperforming the state-of-the-art (SOTA) aggregation algorithms.
  arXiv  Detail & Related papers  (2024-10-23T11:47:04Z)
• Mobility-Aware Federated Self-supervised Learning in Vehicular Network [8.30695698868618]
  Federated Learning (FL) is an advanced distributed machine learning approach. It protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU) This paper proposes a FL algorithm based on image blur level to aggregation, called FLSimCo, which does not require labels and serves as a pre-training stage for self-supervised learning in the vehicular environment.
  arXiv  Detail & Related papers  (2024-08-01T03:28:10Z)
• Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models [0.2999888908665658]
  Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning (ML) models. This work proposes a novel unsupervised FL approach for the identification of potential misbehavior in vehicular environments. We leverage the computing capabilities of public cloud services for model aggregation purposes.
  arXiv  Detail & Related papers  (2024-05-16T08:49:50Z)
• FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units [50.930389484343515]
  Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single RSU mainly focuses on real-time inference and V2X collaboration. Integrating the vast amounts of data from numerous RSUs can provide a rich source of data for model training. We introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
  arXiv  Detail & Related papers  (2024-01-23T15:52:57Z)
• VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning [2.6322811557798746]
  vehicular radio environment map federated learning (VREM-FL) is proposed. It combines mobility of vehicles with 5G radio environment maps. VREM-FL can be tuned to trade training time for radio resource usage.
  arXiv  Detail & Related papers  (2023-11-30T17:38:54Z)
• Robot Fleet Learning via Policy Merging [58.5086287737653]
  We propose FLEET-MERGE to efficiently merge policies in the fleet setting. We show that FLEET-MERGE consolidates the behavior of policies trained on 50 tasks in the Meta-World environment. We introduce a novel robotic tool-use benchmark, FLEET-TOOLS, for fleet policy learning in compositional and contact-rich robot manipulation tasks.
  arXiv  Detail & Related papers  (2023-10-02T17:23:51Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data [42.26599494940002]
  Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model. This paper studies the potential of hierarchical FL in IoT heterogeneous systems. It proposes an optimized solution for user assignment and resource allocation on multiple edge nodes.
  arXiv  Detail & Related papers  (2021-07-14T08:32:39Z)
• PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning [102.36450942613091]
  We propose an inverse reinforcement learning algorithm, called emphinverse temporal difference learning (ITD) We show how to seamlessly integrate ITD with learning from online environment interactions, arriving at a novel algorithm for reinforcement learning with demonstrations, called $Psi Phi$-learning.
  arXiv  Detail & Related papers  (2021-02-24T21:12:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.