Can distributed Kalman filtering improve GP field estimation in WSNs?
Kalman Filter-Based Distributed Gaussian Process for Unknown Scalar Field Estimation in Wireless Sensor Networks
This paper introduces K-DGP, a distributed Gaussian Process (DGP) algorithm for estimating unknown scalar fields (like temperature or signal strength across an area) using a network of sensors. It improves upon existing methods by using a Kalman filter approach, enabling efficient online estimation and handling of dynamic fields. K-DGP also proposes a new "dual-extrema" consensus protocol which allows the sensors to agree on the field estimation more quickly and accurately than traditional methods.
For LLM-based multi-agent systems, this research offers a potential mechanism for distributed knowledge and belief sharing. The efficient consensus protocol and dynamic field estimation capabilities of K-DGP are relevant to scenarios where multiple LLMs need to collaboratively build and update a shared understanding of a changing environment, whether physical or conceptual. The basis function approach to approximating the kernel function could potentially be adapted to manage the complexities of LLM knowledge representation and exchange.