Federated Learning (FL) is an efficient distributed machine learning algorithm that promises to reduce data migration costs to a centralized repository, alleviate regulatory data restrictions and maintain data privacy. However, it suffers from data heterogeneity, i.e., data distribution across silos is often non-identical and independent (non-iid), making optimization difficult. Further, a significant but rarely studied challenge in FL is the lack of annotated data for training. This challenge is more pronounced in the medical field since data annotations require precision and are highly labor-intensive and time-consuming. That is why a few sites have minimal or no annotated data, which wastes valuable data resources for ML training. In this work, we investigate these two challenges of Federated Learning on a publicly available realistic federated medical dataset, KiTS19. First, we explore Federated Learning for Tumor Segmentation task on the Federated version of the KiTS19 dataset for the first time. We show that FL can maintain 96% of model accuracy compared to the centralized model accuracy with ten institution collaboration. In addition, we investigate the benefits of transfer learning to address the challenge of data heterogeneity and show that 5% accuracy improvement is achieved by using a pre-trained model in FL. Moreover, we propose a Federated semi-supervised learning (FSSL) framework to address the challenge of the lack of annotations at some silos. We show that unlabelled silos add 11% to the model’s efficiency compared with the model trained on labeled silos alone.

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few ($2$--$50$) reliable clients, each holding medium to large datasets, and is typically found in applications such as healthcare, finance, or industry. While previous works have proposed representative datasets for cross-device FL, few realistic healthcare cross-silo FL datasets exist, thereby slowing algorithmic research in this critical application. In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.FLamby encompasses 7 healthcare datasets with natural splits, covering multiple tasks, modalities, and data volumes, each accompanied with baseline training code. As an illustration, we additionally benchmark standard FL algorithms on all datasets.Our flexible and modular suite allows researchers to easily download datasets, reproduce results and re-use the different components for their research. FLamby is available at~\url{www.github.com/owkin/flamby}.

Quaternion orthogonal designs (QODs) are considered the foundation of orthogonal space time polarization block codes (OSTPBCs). OSTPBCs benefit from orthogonal polarizations and orthogonal space and time block coding simultaneously to enhance the capacity of wireless communication systems. To exploit these advantages of OSTPBCs, this paper explores two generalized construction techniques of QODs, where the first one is based on symmetric-paired designs while the second technique maps the complex orthogonal designs (CODs) to QODs directly. With these schemes, QODs for any number of transmit antennas can be constructed. Moreover, a low-complexity maximum-likelihood (ML) decoder for the proposed construction techniques has been presented that provides optimal decoupled decoding with phenomenal complexity reduction. Simulation results show that the diversity order of the first QOD construction is higher than the second design given the number of transmit antennas are same.