In　this　paper,　we　consider　the　distributed　inference　for　heterogeneous　linear　models　with　massive　datasets.　Noting　that　heterogeneity　may　exist　not　only　in　the　expectations　of　the　subpopulations,　but　also　in　their　variances,　we　propose　the　heteroscedasticity-adaptive　distributed　aggregation　(HADA)　estimation,　which　is　shown　to　be　communication-efficient　and　asymptotically　optimal,　regardless　of　homoscedasticity　or　heteroscedasticity.　Furthermore,　a　distributed　test　for　parameter　heterogeneity　across　subpopulations　is　constructed　based　on　the　HADA　estimator.　The　finite-sample　performance　of　the　proposed　methods　is　evaluated　using　simulation　studies　and　the　NYC　flight　data.