Computer Vision News - November 2021

73 Amelia Jiménez-Sánchez Curriculum federated learning A potential solution to mitigate the frequent class-imbalance problem and to increase the size of annotated medical datasets is to employ data coming from multiple institutions. Federated Learning (FL) aims to train a machine learning algorithm across multiple decentralized nodes holding samples locally. Training such a decentralized model in a FL setup presents three main challenges: (i) system and statistical heterogeneity, (ii) data protection, and (iii) distributed optimization. (i) We employ federated adversarial learning to deal with the alignment between the different domains. (ii) We leverage differential privacy to handle data protection. (iii) We propose a novel CL strategy for the FL setting (iii). We show that, by monitoring the local and global classification predictions , we can schedule the training samples to boost the alignment between domain pairs and improve the breast cancer classification performance. Training a CNN with CL. Memory-aware curriculum federated learning framework with data privacy protection. (*) Amelia would like to thank her advisors Gemma Piella and Diana Mateus for their support and guidance throughout these years. The work of this thesis would not have been possible without great collaborators and funders (EU’s Horizon 2020Marie Skłodowska Curie programme and “La Caixa” Foundation).