Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data.

BACKGROUND: The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user's data on that user's device. By keeping data on users' devices, federated learning protects users' private data from data leaks and breaches on the researcher's central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. OBJECTIVE: We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. METHODS: We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. RESULTS: In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. CONCLUSIONS: Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity.

In a departure from conventional chemical approaches, data-driven models of chemical reactions have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. To examine the knowledgebase of machine-learning models-what does the machine learn-this article deconstructs black-box machine-learning models of a diverse chemical reaction data set. Through experimentation with chemical representations and modeling techniques, the analysis provides insights into the nature of how statistical accuracy can arise, even when the model lacks informative physical principles. By peeling back the layers of these complicated models we arrive at a minimal, chemically intuitive model (and no machine learning involved). This model is based on systematic reaction-type classification and Evans-Polanyi relationships within reaction types which are easily visualized and interpreted. Through exploring this simple model, we gain deeper understanding of the data set and uncover a means for expert interactions to improve the model's reliability.

Activity-dependent, homeostatic regulation of neurotransmitter release from auditory nerve fibers.

Information processing in the brain requires reliable synaptic transmission. High reliability at specialized auditory nerve synapses in the cochlear nucleus results from many release sites (N), high probability of neurotransmitter release (Pr), and large quantal size (Q). However, high Pr also causes auditory nerve synapses to depress strongly when activated at normal rates for a prolonged period, which reduces fidelity. We studied how synapses are influenced by prolonged activity by exposing mice to constant, nondamaging noise and found that auditory nerve synapses changed to facilitating, reflecting low Pr. For mice returned to quiet, synapses recovered to normal depression, suggesting that these changes are a homeostatic response to activity. Two additional properties, Q and average excitatory postsynaptic current (EPSC) amplitude, were unaffected by noise rearing, suggesting that the number of release sites (N) must increase to compensate for decreased Pr. These changes in N and Pr were confirmed physiologically using the integration method. Furthermore, consistent with increased N, endbulbs in noise-reared animals had larger VGlut1-positive puncta, larger profiles in electron micrographs, and more release sites per profile. In current-clamp recordings, noise-reared BCs had greater spike fidelity even during high rates of synaptic activity. Thus, auditory nerve synapses regulate excitability through an activity-dependent, homeostatic mechanism, which could have major effects on all downstream processing. Our results also suggest that noise-exposed bushy cells would remain hyperexcitable for a period after returning to normal quiet conditions, which could have perceptual consequences.