Federated　Learning　(FL)　is　an　efficient,　distributed　machine　learning　paradigm　that　enables　multiple　clients　to　jointly　train　high-performance　deep　learning　models　while　maintaining　training　data　locally.　However,　due　to　its　distributed　computing　nature,　malicious　clients　can　manipulate　the　prediction　of　the　trained　model　through　backdoor　attacks.　Existing　defense　methods　require　significant　computational　and　communication　overhead　during　the　training　or　testing　phases,　limiting　their　practicality　in　resource-constrained　scenarios　and　being　unsuitable　for　the　Non-IID　data　distribution　typical　in　general　FL　scenarios.　To　address　these　challenges,　we　propose　the　FedPD　framework,　in　which　servers　and　clients　exchange　prototypes　rather　than　model　parameters,　preventing　the　implantation　of　backdoor　channels　by　malicious　clients　during　FL　training　and　effectively　eliminating　the　success　of　backdoor　attacks　at　the　source,　significantly　reducing　communication　overhead.　Additionally,　prototypes　can　serve　as　global　knowledge　to　correct　clients＇　local　training.　Experiments　and　performance　analysis　show　that　FedPD　achieves　superior　and　consistent　defense　performance　compared　to　existing　representative　approaches　against　backdoor　attacks.　In　specific　scenarios,　FedPD　can　reduce　the　success　rate　of　attacks　by　90.73%　compared　to　FedAvg　without　defense　while　maintaining　the　main　task　accuracy　above　90%.