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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