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Edge Intelligence based on federated learning (FL) can be considered to be a promising paradigm for many emerging, strict latency Internet of Things (IoT) applications. Furthermore, a rapid upsurge in the number of IoT devices is expected in the foreseeable future. Although FL enables privacy-preserving, on-device machine learning, it still exhibits a privacy leakage issue. A malicious aggregation server can infer the sensitive information of other end-devices using their local learning model updates. Furthermore, centralized FL aggregation server might stop working due to security attack or a physical damage. To address the aforementioned issues, we propose a novel concept of socially-aware-clustering-enabled dispersed FL. First, we present a novel framework for socially-aware-clustering-enabled dispersed FL. Second, we formulate a problem for minimizing the loss function of the proposed FL scheme. Third …