Federated Learning or FL is the orchestration of centrally connected devices where a pre-trained machine learning model is sent to the devices and the devices train the machine learning model with their own data, individually. Though the data is not being stored in a central database the framework is still prone to data leakage or privacy breach. There are several different privacy attacks on FL such as, membership inference attack, gradient inversion attack, data poisoning attack, backdoor attack, deep learning from gradients attack (DLG). So far different technologies such as differential privacy, secure multi party computation, homomorphic encryption, k-anonymity etc. have been used to tackle the privacy breach. Nevertheless, there is very little exploration on the privacy by design approach and the analysis of the underlying network structure of the seemingly unrelated FL network. Here we are proposing the ΔDSFL framework, where the server is being decoupled into server and an an alyst. Also, in the learning process, ΔDSFL will learn the spatio information from the community detection, and then from DLG attack. Using the knowledge from both the algorithms, ΔDSFL will improve itself. We experimented on three different datasets (geolife trajectory, cora, citeseer) with satisfactory results.