FHC-DQP: Federated Hierarchical Clustering for Distributed QoS Prediction

With the overwhelming explosion of Web services, how to effectively predict unknown QoS has become a key issue of differentiating large-scale similar or functionally equivalent Web services. However, current state-of-the-art QoS prediction approaches based on deep learning still suffer from two deficiencies. First, they mainly focus on predicting vacant QoS in a centralized manner and scarcely take into account distributed QoS prediction, which makes difficult to protect the privacy information of users invoking Web services. Second, they have ignored the hierarchical collaborative relationship to better extract latent features of users and services, reducing the accuracy of QoS prediction. To address these two issues, we propose a novel framework called Federated Hierarchical Clustering for Distributed QoS Prediction (FHC-DQP). It collaboratively performs distributed federated training on independent users' QoS invocations, and then the extracted federated users' private features are fed to clustering algorithm for partitioning them into a set of clusters. By iteratively federated hierarchical clustering, users are fine-grained partitioned together and those users within the same cluster have stronger collaborative relevance for more effectively learning the latent features of users and services leading to the performance improvement of distributed QoS prediction, where contextual-aware deep neural network is designed for personalized QoS prediction. Extensive experiments are conducted based on a public real-world benchmarking dataset called WS-DREAM with almost 2,000,000 user-service historical QoS invocations. Compared with both centralized and federated competing baselines, the results demonstrate FHC-DQP receives superior performance for distributed QoS prediction, when it provides privacy-preserving of users' QoS invocations.