Call for paper

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Main research topics of relevance to this workshop include, but are not limited to:
  • Novel FL models for computer vision tasks, e.g., scene understanding, face recognition, object detection, person re-identification, image segmentation, human action recognition, medical image processing, etc.
  • Privacy-preserving machine learning for computer vision tasks
  • Personalized FL models for computer vision applications
  • Novel computer vision applications of FL and privacy-preserving machine learning
  • FL frameworks and tools designed for computer vision applications and benchmarking
  • Novel vision datasets for FL
  • Optimization algorithms for FL, particularly algorithms tolerant of data heterogeneity and resource heterogeneity
  • Approaches that scale FL to larger models, including model pruning and gradient compression techniques
  • Label efficient learning in FL, e.g., self-supervised learning, semi-supervised learning, active learning, etc.
  • Neural architecture search (NAS) for FL
  • Life-long learning in FL
  • Attacks on FL including model poisoning, data poisoning, and corresponding defenses
  • Fairness in FL
  • Federated domain adaptation
  • Privacy leakage and defense in the FL environments
  • Privacy-preserving Generative models for CV
  • FL based CV pipeline for scene understanding and visual analytics