FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning

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. Monitoring a large population of dynamic processes within the constraints of monitoring resources poses a significant challenge across various industrial sectors, including healthcare and engineering systems [1], [2]. The complexity arises from two key factors: 1) the inherent disparity between the limited monitoring resources available and the large population of processes to be monitored, and 2) the uncertain and heterogeneous dynamics in the progression of these processes. In tackling this intricate problem, online learning from bandit feedback has demonstrated notable potential [2], [3].