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This paper presents novel Gaussian process decentralized data fusionalgorithms exploiting the notion of agent-centric support sets for distributedcooperative perception of large-scale environmental phenomena. To overcome thelimitations of scale in existing works, our proposed algorithms allow everymobile sensing agent to choose a different support set and dynamically switchto another during execution for encapsulating its own data into a local summarythat, perhaps surprisingly, can still be assimilated with the other agents'local summaries (i.e., based on their current choices of support sets) into aglobally consistent summary to be used for predicting the phenomenon. Toachieve this, we propose a novel transfer learning mechanism for a team ofagents capable of sharing and transferring information encapsulated in asummary based on a support set to that utilizing a different support set withsome loss that can be theoretically bounded and analyzed. To alleviate theissue of information loss accumulating over multiple instances of transferlearning, we propose a new information sharing mechanism to be incorporatedinto our algorithms in order to achieve memory-efficient lazy transferlearning. Empirical evaluation on real-world datasets show that our algorithmsoutperform the state-of-the-art methods.

Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception