AI RESEARCH
Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information
arXiv CS.LG
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ArXi:2605.02705v1 Announce Type: new Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide whether to participate in exchange for money. The MCS system is dynamic: the task requirements, the MUs' availability, and their available resources change over time. The MUs aim to find an efficient task participation strategy to maximize their income while the MCSP focuses on maximizing the number of completed tasks.