mlsanityData to 14 Oct 2026

FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving

arXiv 2309.13336 ↗First posted 23 Sep 2023cs.CVcited by 3

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Abstract (arXiv)

We propose FedDrive v2, an extension of the Federated Learning benchmark for Semantic Segmentation in Autonomous Driving. While the first version aims at studying the effect of domain shift of the visual features across clients, in this work, we focus on the distribution skewness of the labels. We propose six new federated scenarios to investigate how label skewness affects the performance of segmentation models and compare it with the effect of domain shift. Finally, we study the impact of using the domain information during testing. Official website: https://feddrive.github.io

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 23 Sep 2023.

Builds on

16 of 22 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  4. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  5. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  6. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
  7. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  8. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  9. 2020Federated Visual Classification with Real-World Data Distributioncited by 128
  10. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 109
  11. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
  12. 2021Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentationcited by 44
4 more
  1. 2021Federated Multi-Target Domain Adaptationcited by 44
  2. 2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Drivingcited by 41
  3. 2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
  4. 2020IDDA: a large-scale multi-domain dataset for autonomous drivingcited by 31

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifierscited by 10
  2. 2024Collaborative Visual Place Recognition through Federated Learningcited by 2
  3. 2024FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehiclescited by 2