Federated Learning Under Restricted User Availability
Periklis Theodoropoulos Konstantinos E. Nikolakakis Dionysis Kalogerias
Federated Learning (FL) is a decentralized machine learning framework that
enables collaborative model training while respecting data privacy. In various
applications, non-uniform availability or participation of users is unavoidable
due to an adverse or stochastic environment, the latter often being
uncontrollable during learning. Here, we posit a generic user selection
mechanism implementing a possibly randomized, stationary selection policy,
suggestively termed as a Random Access Model (RAM). We propose a new
formulation of the FL problem which effectively captures and mitigates limited
participation of data originating from infrequent, or restricted users, at the
presence of a RAM. By employing the Conditional Value-at-Risk (CVaR) over the
(unknown) RAM distribution, we extend the expected loss FL objective to a
risk-aware objective, enabling the design of an efficient training algorithm
that is completely oblivious to the RAM, and with essentially identical
complexity as FedAvg. Our experiments on synthetic and benchmark datasets show
that the proposed approach achieves significantly improved performance as
compared with standard FL, under a variety of setups.