Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. Training on data centers thus requires higher communication resource/energy demands while violating privacy. This is considered today as a significant bottleneck in pursuing scientific collaboration across trans-national clinical medical research centers. Recently, federated learning (FL) has emerged as a distributed AI approach that enables the cooperative training of ML models, without the need of sharing patient data. This paper dives into the analysis of different FL methods and proposes a real-time distributed networking framework based on the Message Queuing Telemetry Transport (MQTT) protocol. In particular, we design a number of solutions for ML over networks, based on FL tools relying on a parameter server (PS) and fully decentralized paradigms driven by consensus methods. The proposed approach is validated in the context of brain tumor segmentation, using a modified version of the popular U-NET model with representative clinical datasets obtained from the daily clinical workflow. The FL process is implemented on multiple physically separated machines located in different countries and communicating over the Internet. The real-time test-bed is used to obtain measurements of training accuracy vs. latency trade-offs, and to highlight key operational conditions that affect the performance in real deployments.
* LHC DivisionPresented at CHATS'97, San Francisco, USA 23-25 July 1997 Administrative Secretariat LHC Division CERN CH -1211 Geneva 23 Switzerland Geneva, 17 November 1997In preparation for the Large Hadron Collider project, a 42.5 m-long prototype superconducting magnet string, representing a half-cell of the machine lattice, has been built and operated. A series of tests was performed to assess the thermohydraulics of resistive transitions (quenches) of the superconducting magnets. These measurements provide the necessary foundation for describing the observed evolution of the helium in the cold mass and formulating a mathematical model based on energy conservation. The evolution of helium after a quench simulated with the model reproduces the observations. We then extend the simulations to a full LHC cell, and finally analyse the recovery of helium discharged from the cold mass. In preparation for the Large Hadron Collider project, a 42.5 m-long prototype superconducting magnet string, representing a half-cell of the machine lattice, has been built and operated. A series of tests was performed to assess the thermohydraulics of resistive transitions (quenches) of the superconducting magnets. These measurements provide the necessary foundation for describing the observed evolution of the helium in the cold mass and formulating a mathematical model based on energy conservation. The evolution of helium after a quench simulated with the model reproduces the observations. We then extend the simulations to a full LHC cell, and finally analyse the recovery of helium discharged from the cold mass.
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