Fedcafe: Federated Context-Aware Recommendation Via Adaptive Fuzzy Embedding

Xiaoyu Kang, Zhixin Shi

Abstract: Mobile edge computing (MEC) is important in location-based social networks (LBSNs). It puts services near users to cut delays. Edge service recommendation needs to mix context details with user privacy. Data sparsity makes this hard. Traditional methods have trouble with little data. They miss small context details or hurt privacy with central systems. This paper introduces FedCAFE, a federated learning system for edge service recommendation with context awareness. FedCAFE uses three main parts. It has a denoising autoencoder to get strong user and service features from small data. This tool learns patterns by fixing noisy information. It helps when user-service interactions are few. FedCAFE also uses a new adaptive fuzzy clustering method to group users and services by context matches. This part looks at things like time and place. It changes how it groups based on different situations. FedCAFE applies federated learning to keep privacy safe. It trains on user devices. It sends only model updates, not personal data. This stops private stuff like location from leaving the device. We tested FedCAFE on the WSDream dataset with real service information. FedCAFE beats other methods in these tests.

External IDs:dblp:conf/mdm/KangS25