Aerobic capacity and upper extremity strength in women diagnosed with breast cancer are generally lower than population norms. Assessment of values for lower extremity strength is less conclusive. As more research is published, expected values for sub-groups by age, treatment, and co-morbidities should be developed. [Neil-Sztramko SE, Kirkham AA, Hung SH, Niksirat N, Nishikawa K Campbell KL (2014) Aerobic capacity and upper limb strength are reduced in women diagnosed with breast cancer: a systematic review.Journal of Physiotherapy60: 189-200].

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Our results suggest that PSTP is feasible among women with breast cancer for early identification of arm morbidity. A larger study is needed to determine the cost and effectiveness benefits.

We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In recent years, support for an additional federated task has been added: federated learning (FL) of deep neural networks. To our knowledge, only one other system has been described in literature that supports FL at scale. We include comparisons to that system to help discuss design decisions and attached trade-offs. Finally, we describe two specific large scale personalization use cases in detail to showcase the applicability of federated tuning to on-device personalization and to highlight application specific solutions.