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Federated Learning-Based Distributed Data Completion in Sparse Mobile CrowdSensing
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DOI：10.1109/TMC.2025.3640170.png)
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Abstract
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Sparse Mobile CrowdSensing (SMCS) is an emerging distributed data collection framework. As one of the core methods,data completion uses the collected data to fill in the missing data. However, this approach inevitably poses significant privacy risks, since the traditional data completion methods require users’ time and location information. In this paper, we propose a federated learning-based distributed data completion framework, which employs matrix factorization (MF) for local completion model training and federated learning to aggregate parameters of the MF model. This enables the construction of a global completion model without requiring private data, thereby mitigating privacy concerns. To address the challenges posed by the sparsity and asynchrony of distributed data, we incorporate time-aware deep neural network architectures with an asynchronous mechanism to enable federated training under irregularly gathered local data. Experimental results demonstrate that the proposed model achieves high data completion accuracy while ensuring robust privacy protection.
Keywords:
Privacy protection
distributed data
federated learning
sparse crowdsensing
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