The　privacy　and　security　of　data　are　recently　research　hotspots　and　challenges.　For　this　issue,　an　adaptive　scheme　of　distributed　learning　based　on　homomorphic　encryption　and　blockchain　is　proposed.　Specifically,　in　the　form　of　homomorphic　encryption,　the　computing　party　iteratively　aggregates　the　learning　models　from　distributed　participants,　so　that　the　privacy　of　both　the　data　and　model　is　ensured.　Moreover,　the　aggregations　are　recorded　and　verified　by　blockchain,　which　prevents　attacks　from　malicious　nodes　and　guarantees　the　reliability　of　learning.　For　these　sophisticated　privacy　and　security　technologies,　the　computation　cost　and　energy　consumption　in　both　the　encrypted　learning　and　consensus　reaching　are　analyzed,　based　on　which　a　joint　optimization　of　computation　resources　allocation　and　adaptive　aggregation　to　minimize　loss　function　is　established　with　the　realistic　solution　followed.　Finally,　the　simulations　and　analysis　evaluate　the　performance　of　the　proposed　scheme.　©　2022　Inst.　of　Scientific　and　Technical　Information　of　China.　All　rights　reserved.