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- W4313000908 abstract "Smart homes IoT (SHIoT) deploys various devices that can achieve rich functionality by invoking cloud services for better in-home experiences. Recommending the quality services will allow the devices to provide greater comfort to the homeowner. Traditional service recommendation methods require collecting the quality of service (QoS) data to achieve better recommendations but also pose privacy problems. Inspired by federated learning (FL), some privacy-preserving federated recommendation methods have been proposed. However, studies have demonstrated that user preferences and even raw data can be inferred in FL. To implement service recommendations in SHIoT while protecting the edge privacy, we propose a secure federated QoS prediction method with neighbor collaboration (NCSF). By collaboration, users in NCSF upload perturbed updates, while the perturbations are offset during server aggregation without affecting the global model. Experiments on real-world datasets show that NCSF can achieve same recommendation quality and stronger privacy protection as the FL approach of plaintext aggregation." @default.
- W4313000908 created "2023-01-05" @default.
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- W4313000908 date "2022-01-01" @default.
- W4313000908 modified "2023-09-30" @default.
- W4313000908 title "Neighbor Collaboration-Based Secure Federated QoS Prediction for Smart Home Services" @default.
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- W4313000908 doi "https://doi.org/10.1007/978-3-031-23515-3_6" @default.
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