Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the
【ICLR 2022】On Bridging Generic and Personalized Federated Learning for Image Classification FL的标准设置寻求训练能够很好地处理通用数据分布的单一的“全局”模型(generic FL) FL的另一种设置试图通过为每个客户构建与客户的个性化数据捆绑在一起的“个性化”模型来承认客户之间的异质性(personalized FL)(为每个客户...
图像分类 Image Classification 多模态数据集 Multimodal Federated Learning is a collaborative training process involving multiple clients, each with diverse modality settings and data, conducting learning tasks without disclosing their local raw data. 多模态联邦学习(Multimodal Federated Learning, MMFL)是一种涉...
The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly availabl
19 showed that for an image classification task, the performance of their data-private collaborative models dropped by up to 55% depending on how much institutional bias (degree of non-IID) they introduce when sharding (i.e., partitioning) a single dataset into hypothetical institutions. The ...
Data heterogeneity across medical centers, resulting in a coupling of universal information for classification tasks and personalized information for private dataset within local models, is still a difficult challenge in personalized federated learning (PFL). Moreover, the high interclass similarity in the...
federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suitable for federated learning. FLAIR has 429,078 images from ...
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On Bridging Generic and Personalized Federated Learning for Image Classification The Ohio State University code Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond KAIST;MIT ICLR 2021 Federated Learning Based on Dynamic Regularization Boston University;ARM Achieving Linear Speedup ...
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses Hyperparameter Optimizatio...