Before revision:  The experimental results show that Fed-NILM  outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
After revision:  The experimental results show that Fed-NILM outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modeling capabilities of local data owners while protecting power consumers' privacy .