Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates

The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train XGBoost in the context of federated learning (FL). Existing works on federated XGBoost in the horizontal setting rely on the sharing of gradients, which induce per-node level communication frequency and serious privacy concerns. To alleviate these problems, we develop an innovative framework for horizontal federated XGBoost which does not depend on the sharing of gradients and simultaneously boosts privacy and communication efficiency by making the learning rates of the aggregated tree ensembles learnable. We conduct extensive evaluations on various classification and regression datasets, showing our approach achieves performance comparable to the state-of-the-art method and effectively improves communication efficiency by lowering both communication rounds and communication overhead by factors ranging from 25x to 700x.Comment: Accepted at the 3rd ACM Workshop on Machine Learning and Systems (EuroMLSys), May 8th 2023, Rome, Ital

A first look into the carbon footprint of federated learning

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in datacenters. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show that FL, despite being slower to converge in some cases, may result in a comparatively greener impact than a centralized equivalent setup. We performed extensive experiments across different types of datasets, settings, and various deep learning models with FL. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.Comment: arXiv admin note: substantial text overlap with arXiv:2010.0653

Medical appointments and provision of medical care during the COVID-19 pandemic in Mainz, Germany

Previous evidence suggested that non-COVID-19-related medical care was reduced during the first wave of the COVID-19 pandemic, but it remained unclear whether or to which extent this effect lasted beyond the first wave, or existed in a longer time frame. Here, we consider questionnaire data of the Gutenberg-COVID-19 study together with pre-pandemic baseline data of the Gutenberg Health Study concerning the region around Mainz, Germany, to study the effects of the pandemic on the provision of medical care until April 2021. We observed that the proportion of cancelled medical appointments was low and that the fraction of participants with a medical appointment as an indicator for the number of appointments being made was in line with pre-pandemic levels. Appointments were more likely cancelled by the patient (rather than the provider), and more likely cancelled by medical specialists such as dentists or ophthalmologists (rather than GPs). In conclusion, we found some evidence that, at least with regard to realized appointments, the medical system and the provision of medical care were not harmed by the COVID-19 pandemic on a longer time scale

Prevention of radiochemotherapy-induced toxicity with amifostine in patients with malignant orbital tumors involving the lacrimal gland: a pilot study

BACKGROUND: To use amifostine concurrently with radiochemotherapy (CT-RT) or radiotherapy (RT) alone in order to prevent dry eye syndrome in patients with malignancies located in the fronto-orbital region. METHODS: Five patients (2 males, 3 females) with diagnosed malignancies (Non-Hodgkin B-cell Lymphoma, neuroendocrine carcinoma) involving the lacrimal gland, in which either combined CT-RT or local RT were indicated, were prophylactically treated with amifostine (500 mg sc). Single RT fraction dose, total dose and treatment duration were individually adjusted to the patient's need. Acute and late adverse effects were recorded using the RTOG score. Subjective and objective dry eye assessment was performed for the post-treatment control of lacrimal gland function. RESULTS: All patients have completed CT-RT or RT as indicated. The median total duration of RT was 29 days (range, 23 - 39 days) and the median total RT dose was 40 Gy (range, 36 - 60 Gy). Median lacrimal gland exposure was 35.9 Gy (range, 16.8 - 42.6 Gy). Very good partial or complete tumor remission was achieved in all patients. The treatment was well tolerated without major toxic reactions. Post-treatment control did not reveal in any patient either subjective or objective signs of a dry eye syndrome. CONCLUSION: The addition of amifostine to RT/CT-RT of patients with tumors localized in orbital region was found to be associated with absence of dry eye syndrome

A decade of detailed observations (2008-2018) in steep bedrock permafrost at the Matterhorn Hörnligrat (Zermatt, CH)

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines and started in 2006 with the goals of realizing observations that previously have not been possible. Specifically, the aims are to obtain measurements in unprecedented quantity and quality based on technological advances. This paper describes a unique >10-year data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500ma:s:l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make these data available in near real time and tightly monitor and control the running experiments. This data set (https://doi.org/10.1594/PANGAEA.897640,Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of 10 or more years. By documenting and sharing these data in this form we contribute to making our past research reproducible and facilitate future research based on these data, e.g., in the areas of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. Finally, the cross-validation of four different data types clearly indicates the dominance of thawing-related kinematics

Federated Benchmarking of Medical Artificial Intelligence With MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform

MedPerf : Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform