A Privacy Protection Method for IoT Data Based on Edge Computing and Federated Learning Algorithm

This paper proposes a privacy protection method for IoT data integrating edge computing and federated learning. To address challenges including edge node heterogeneity, central server bottlenecks in traditional federated learning, and high overhead of homomorphic encryption, we design a hierarchical architecture comprising requesters, participants, edge nodes, a sensing platform, and a key generation center. Participants train models locally using SGD, encrypt parameters with an optimized verifiable dual-key ElGamal homomorphic encryption scheme, and transmit them to edge nodes. Edge nodes employ the MPSDGS algorithm for participant similarity discovery and dropout supplementation, and the MP-Update method for dynamic weighted averaging to ensure continuity and accuracy. Edge-side ciphertext aggregation reduces data volume to the platform. The sensing platform performs global secure aggregation in ciphertext. Experiments demonstrate that the method maintains data privacy above 0.8, with training and aggregation delays within acceptable ranges for typical IoT scales, balancing privacy and efficiency.

@article{Wu2025,
  title     = {A Privacy Protection Method for IoT Data Based on Edge Computing and Federated Learning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ying Wu},
  doi       = {10.14569/IJACSA.2025.0161179},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161179}
}