A Privacy-Preserved Online Personalized Federated Learning Framework for Indoor Localization

By taking advantage of Deep Learning (DL), Received signal strength (RSS) fingerprint-based indoor localization has attracted more attention. Training DL models require an immense amount of RSS samples, and crowdsourcing has been another way to collect data by users. Recently, some researchers apply Federated Learning (FL) in indoor localization instead of crowdsourcing for privacy protection. However, more practical issues are still not considered. For further guaranteeing privacy and deploying systems in real time, this paper proposes OPFL, a privacy-preserved online personalized federated learning framework for fingerprint-based indoor localization. In OPFL, users update model parameters and collect new labeled samples meanwhile. By adding artificial noise based on the Differential Privacy (DP) mechanism, it is harder for attackers to infer information. Besides, the deterioration of models results from DP is mitigating by applying the personalized algorithm. The experimental results show that under the noise perturbing, the performance of OPFL testing on the local test set is far better than the conventional federated averaging algorithm (FedAvg), and is even close to the centralized training.