  This study addresses the challenge of online learning in contexts where
agents accumulate disparate data, face resource constraints, and use different
local algorithms. This paper introduces the Switched Online Learning Algorithm
(SOLA), designed to solve the heterogeneous online learning problem by
amalgamating updates from diverse agents through a dynamic switching mechanism
contingent upon their respective performance and available resources. We
theoretically analyze the design of the selecting mechanism to ensure that the
regret of SOLA is bounded. Our findings show that the number of changes in
selection needs to be bounded by a parameter dependent on the performance of
the different local algorithms. Additionally, two test cases are presented to
emphasize the effectiveness of SOLA, first on an online linear regression
problem and then on an online classification problem with the MNIST dataset.
