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Federated AI for Real-World Business Scenarios
Real-world AI applications frequently have training data distributed in many different locations, with data at different sites having different properties and different formats. In many cases, data movement is not permitted due to security concerns, bandwidth, cost or regulatory restriction. Under these conditions, techniques of federated learning can enable creation of practical applications. Creating practical applications requires implementation of the cycle of learning from data, inferring from data, and acting based on the inference.
Bio-inspired computing and communication ; 1st Workshop on Bio-inspired design of networks, BIOWIRE 2007 Cambridge, UK, April 2-5, 2007 Revised Selected Papers
The book constitutes the thoroughly refereed post-workshop proceedings of the First Workshop on Bio-Inspired Design of Networks, BIOWIRE 2007, held in Cambridge, UK, in April 2007.
