Federated Markov Logic Network for indoor activity recognition in Internet of Things

Indoor activity recognition is essential in numerous Internet of Things (IoT) applications. As one of the widely used methods in this domain, Markov Logic Network (MLN) can simultaneously use activity knowledge and data by unifying probability and logic. The “cloud computing” model has recently been adopted to concentrate the activity data and activity knowledge in a central node for processing in indoor activity recognition by using MLN, which may lead to the data leakage of the clients. Therefore, to further alleviate client data privacy issues when building an indoor activity recognition model by training MLN, this paper proposes a Federated Markov Logic Network (FMLN) framework for indoor activity recognition. We designed different scenarios to investigate the FMLN framework, including statistical heterogeneity, the number ofvarious clients, and various network environments. The experimental results show that the FMLN framework effectively detects indoor activity.