Federated Multi-task Learning in Distributed Networks

We consider a Distributed Federated multitask learning scheme that accounts for multiple linear model estimation tasks with heterogeneous and/or correlated data streams. We assume that nodes can be partitioned into groups corresponding to different learning tasks and communicate according to a directed network topology. Each node estimates a linear model asynchronously and is subject to local (within-group) regularization and global (across groups) regularization terms targeting noise reduction and generalization performance improvement, respectively. We provide a finite-time characterization of the convergence of the estimators and task relation and illustrate the scheme’s general applicability of random field temperature estimation.