As　many　countries　have　promulgated　laws　to　protect　users＇　data　privacy,　how　to　legally　use　users＇　data　has　become　a　hot　topic.　With　the　emergence　of　federated　learning　(FL)　(also　known　as　collaborative　learning),　multiple　participants　can　create　a　common,　robust,　and　secure　machine　learning　model　while　addressing　key　issues　in　data　sharing,　such　as　privacy,　security,　accessibility,　etc.　Unfortunately,　existing　research　shows　that　FL　is　not　as　secure　as　it　claims,　gradient　leakage　and　the　correctness　of　aggregation　results　are　still　key　problems.　Recently,　some　scholars　try　to　address　these　security　problems　in　FL　by　cryptography　and　verification　techniques.　However,　there　are　some　issues　in　this　scheme　that　remain　unsolved.　First,　some　solutions　cannot　guarantee　the　correctness　of　the　aggregation　results.　Second,　existing　state-of-the-art　FL　schemes　have　a　costly　computational　and　communication　overhead.　In　this　article,　we　propose　SVFLC,　a　secure　and　verifiable　FL　scheme　with　chain　aggregation　to　solve　these　problems.　We　first　design　a　privacy-preserving　method　that　can　solve　the　problem　of　gradient　leakage　and　defend　against　collusion　attacks　by　semi-honest　users.　Then,　we　create　a　verifiable　method　based　on　a　homomorphic　hash　function,　which　can　ensure　the　correctness　of　the　weighted　aggregation　results.　Besides,　the　SVFLC　can　also　track　users　who　encounter　calculation　errors　during　the　aggregation　process.　Additionally,　the　extensive　experiment　results　on　real-world　data　sets　demonstrate　that　the　SVFLC　is　efficient,　compared　with　other　solutions.　　©　2014　IEEE.