In　the　era　emphasizing　the　privacy　of　personal　data,　verifiable　federated　learning　has　garnered　significant　attention　as　a　machine　learning　approach　to　safeguard　user　privacy　while　simultaneously　validating　aggregated　result.　However,　there　are　some　unresolved　issues　when　deploying　verifiable　federated　learning　in　edge　computing.　Due　to　the　constraint　resources,　edge　computing　demands　cost　saving　measurements　in　model　training　such　as　model　pruning.　Unfortunately,　there　is　currently　no　protocol　capable　of　enabling　users　to　verify　pruning　results.　Therefore,　in　this　paper,　we　introduce　PrVFL,　a　verifiable　federated　learning　framework　that　supports　model　pruning　verification　and　heterogeneous　edge　computing.　In　this　scheme,　we　innovatively　utilize　zero-knowledge　range　proof　protocol　to　achieve　pruning　result　verification.　Additionally,　we　first　propose　a　heterogeneous　delayed　verification　scheme　supporting　the　validation　of　aggregated　result　for　pruned　heterogeneous　edge　models.　Addressing　the　prevalent　scenario　of　performance-heterogeneous　edge　clients,　our　scheme　empowers　each　edge　user　to　autonomously　choose　the　desired　pruning　ratio　for　each　training　round　based　on　their　specific　performance.　By　employing　a　global　residual　model,　we　ensure　that　every　parameter　has　an　opportunity　for　training.　The　extensive　experimental　results　demonstrate　the　practical　performance　of　our　proposed　scheme.　IEEE