  Online learning has demonstrated notable potential to dynamically allocate
limited resources to monitor a large population of processes, effectively
balancing the exploitation of processes yielding high rewards, and the
exploration of uncertain processes. However, most online learning algorithms
were designed under 1) a centralized setting that requires data sharing across
processes to obtain an accurate prediction or 2) a homogeneity assumption that
estimates a single global model from the decentralized data. To facilitate the
online learning of heterogeneous processes from the decentralized data, we
propose a federated collaborative online monitoring method, which captures the
latent representative models inherent in the population through representation
learning and designs a novel federated collaborative UCB algorithm to estimate
the representative models from sequentially observed decentralized data. The
efficiency of our method is illustrated through theoretical analysis,
simulation studies, and decentralized cognitive degradation monitoring in
Alzheimer's disease.
