Federated　learning　is　a　machine　learning　prgadigm　that　enables　the　collaborative　learning　among　clients　while　keeping　the　privacy　of　clients＇　data.　Federated　multitask　learning　(FMTL)　deals　with　the　statistic　challenge　of　non-independent　and　identically　distributed　(IID)　data　by　training　a　personalized　model　for　each　client,　and　yet　requires　all　the　clients　to　be　always　online　in　each　training　round.　To　eliminate　the　limitation　of　full-participation,　we　explore　multitask　learning　associated　with　model　clustering,　and　first　propose　a　clustered　FMTL　to　achieve　the　multual-task　learning　on　non-IID　data,　while　simultaneously　improving　the　communication　efficiency　and　the　model　accuracy.　To　enhance　its　privacy,　we　adopt　a　general　dual-server　architecture　and　further　propose　a　secure　clustered　FMTL　by　designing　a　series　of　secure　two-party　computation　protocols.　The　convergence　analysis　and　security　analysis　is　conducted　to　prove　the　correctness　and　security　of　our　methods.　Numeric　evaluation　on　public　data　sets　validates　that　our　methods　are　superior　to　state-of-the-art　methods　in　dealing　with　non-IID　data　while　protecting　the　privacy.