Summary
Distributed intelligence with large amounts of local measurement data hinges on the design of devices that are capable of sensing, processing, and exchanging local information. Reliable decision-making in a collective manner is, however, a challenging task in resource-constrained scenarios, where devices are limited by communication costs and/or processing power. This project aims to develop a new framework for designing efficient decentralized algorithms such that the average proportion of wrong decisions in the network is bounded by a prescribed target threshold. This line of research has im
