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A key functionality of emerging connected autonomous systems such as smartcities, smart transportation systems, and the industrial Internet-of-Things, isthe ability to process and learn from data collected at different physicallocations. This is increasingly attracting attention under the terms ofdistributed learning and federated learning. However, in connected autonomoussystems, data transfer takes place over communication networks with oftenlimited resources. This paper examines algorithms for communication-efficientlearning for linear regression tasks by exploiting the informativeness of thedata. The developed algorithms enable a tradeoff between communication andlearning with theoretical performance guarantees and efficient practicalimplementations.

Linear Regression over Networks with Communication Guarantees