FUSED is a proposed method for federated unlearning that addresses challenges such as indiscriminate knowledge removal and the irreversibility of unlearning. It utilizes selective sparse adapters to overwrite sensitive knowledge without altering original model parameters, making unlearning both reversible and cost-effective. Experimental results indicate that FUSED outperforms existing methods while significantly reducing unlearning costs.