Decentralized online algorithms for random inverse problems on graphs in Hilbert and RKHS spaces are proposed with proofs of mean-square and almost sure strong consistency under graph connectivity and infinite-dimensional spatio-temporal persistence of excitation.
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Decentralized Online Learning for Random Inverse Problems Over Graphs
Decentralized online algorithms for random inverse problems on graphs in Hilbert and RKHS spaces are proposed with proofs of mean-square and almost sure strong consistency under graph connectivity and infinite-dimensional spatio-temporal persistence of excitation.