Federated Learning enables visual models to be trained ondevice, bringing advantages for user privacy (data need never leave the device), but challenges in terms of data diversity and quality. Whilst typical models in the datacenter are trained using data that are independent and identically distributed (IID), data at source are typically far from IID. Furthermore, differing quantities of data are typically available at each device (imbalance). In this work, we characterize the effect these real-world data distributions have on distributed learning, using as a benchmark the standard Federated Averaging (FedAvg) algorithm. To do so, we introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios. We also develop two new algorithms (FedVC, FedIR) that intelligently resample and reweight over the client pool, bringing large improvements in accuracy and stability in training.

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Background Obesity has been linked to severe clinical outcomes among people who are hospitalized with COVID-19. We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). Methods We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with PCR-confirmed SARS-CoV-2 infection from March 11 - May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from CT scans during or prior to the hospital admission. VAT quantification took an average 2±0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI and diabetes status. Results 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared to those with low VAT (p<0.005), especially in those with BMI <30 kg/m 2 (p<0.005). In multivariable models, the aHR for high vs. low VAT was unchanged [aHR 1.97 (1.24 – 3.09)], whereas BMI was no longer significant [aHR for obese vs. normal BMI 1.14 (0.71 – 1.82)]. Conclusions High VAT is associated with a greater risk of severe disease or death in COVID-19, and can offer more precise information to risk stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.

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