Safety　helmet　wearing　detection　based　on　video　surveillance　is　an　important　means　of　safety　monitoring　in　many　industrial　scenes.　The　training　of　safety　helmet　wearing　detection　models　requires　large　and　well-labeled　dataset.　However,　the　incidence　of　security　violations　is　relatively　low,　which　results　in　insufficient　samples　for　training　deep　detection　models.　Safety　helmet　wearing　detection　is　a　common　requirement　in　many　scenarios　such　as　construction　sites,　substations,　and　factory　workshops.　Aggregating　data　from　multiple　companies　for　model　training　would　improve　the　performance　of　the　detection　model.　Traditional　centralized　training　methods　are　not　feasible　because　aggregating　data　in　centralized　locations　(such　as　the　cloud)　can　raise　concerns　about　data　privacy　and　the　high　cost　of　data　communication　and　storage.　This　paper　proposes　FedSH,　a　novel　cloud-edge-based　federated　learning　framework,　which　learns　a　shared　global　safety　helmet　wearing　detection　model　in　the　cloud　from　multiple　companies　at　the　network　edges　and　achieves　data　privacy　protection　by　keeping　company　data　locally.　In　addition,　this　paper　designs　reweighting　mechanisms　and　applies　transfer　learning　to　address　class　imbalance　and　non-IID　problems　in　the　training　data,　so　as　to　obtain　an　accurate　and　personalized　detection　model.　Extensive　experiments　have　been　conducted　on　real　surveillance　video　datasets.　The　experimental　results　demonstrate　that　FedSH　outperforms　the　existing　widely　used　federated　learning　methods　with　an　accuracy　improvement　of　at　least　3.4%;　the　reduction　in　accuracy　is　within　the　range　of　5%　compared　with　centralized　learning　methods.　FedSH　effectively　achieves　a　good　balance　between　model　performance,　privacy　protection,　and　communication　efficiency.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2024.