This paper presents the collaborative agent-based learning subsystem of HealthAgents, a multiagent distributed decision support system for brain tumour diagnosis. The subsystem aims to boost the performance of the independent and heterogeneous classifiers in spite of the limited data transfer conditions prevailing in the system. The susbsystem is composed by local autonomous agents which are interacting among them, following an existing collaborative learning model. The different aspects and decisions dodged during the adaptation of this model are described in addition to the results of its initial evaluation with the data of HealthAgents. Significant increments of classification performance attained by the learning agents demonstrate the potential benefits of this subsystem.