Random　forest　is　one　of　the　most　heated　machine　learning　tools　in　a　wide　range　of　industrial　scenarios.　Recently,　federated　learning　enables　efficient　distributed　machine　learning　without　direct　revealing　of　private　participant　data.　In　this　article,　we　present　a　novel　framework　of　federated　random　forest　(RevFRF),　and　further　emphatically　discuss　the　participant　revocation　problem　of　federated　learning　based　on　RevFRF.　Specifically,　RevFRF　first　introduces　a　suite　of　homomorphic　encryption　based　secure　protocols　to　implement　federated　random　forest　(RF).　The　protocols　cover　the　whole　lifecycle　of　an　RF　model,　including　construction,　prediction　and　participant　revocation.　Then,　referring　to　the　practical　application　scenarios　of　RevFRF,　the　existing　federated　learning　frameworks　ignore　a　fact　that　even　every　participant　in　federated　learning　cannot　maintain　the　cooperation　with　others　forever.　In　company-level　cooperation,　allowing　the　remaining　companies　to　use　a　trained　model　that　contains　the　memories　from　an　off-lying　company　potentially　leads　to　a　significant　conflict　of　interest.　Therefore,　we　propose　the　revocable　federated　learning　concept　and　illustrate　how　RevFRF　implements　participant　revocation　in　applications.　Through　theoretical　analysis　and　experiments,　we　show　that　the　protocols　can　efficiently　implement　federated　RF　and　ensure　the　memories　of　a　revoked　participant　in　the　trained　RF　to　be　securely　removed.