Signal Quantization and Approximation Algorithms for Federated Learning (2022)
Abstract / truncated to 115 words
Distributed signal or information processing using Internet of Things (IoT), facilitates real-time monitoring of signals, for example, environmental pollutants, health indicators, and electric energy consumption in a smart city. Despite the promising capabilities of IoTs, these distributed deployments often face the challenge of data privacy and communication rate constraints. In traditional machine learning, training data is moved to a data center, which requires massive data movement from distributed IoT devices to a third-party location, thus raising concerns over privacy and inefficient use of communication resources. Moreover, the growing network size, model size, and data volume combined lead to unusual complexity in the design of optimization algorithms beyond the compute capability of a single device. This ... toggle 25 keywordsapproximation theory – learning (artificial intelligence) – internet of things – quantisation (signal) – internet – calibration – client-server systems – cloud computing – convergence – convex programming – data privacy – health care – image classification – inference mechanisms – intelligent sensors – iterative methods – mean square error methods – medical computing – medical information systems – optimisation – patient monitoring – pattern classification – piecewise linear techniques – probability – signal classification
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