Performance analysis of blockchain-based secured distributed deep federated learning for wearable internet of things

Performance analysis of blockchain-based secured distributed deep federated learning for wearable internet of things

연구 분야: Databases

학회: Cluster Computing

With the use of wearable technology, we can now gain useful data-driven insights into our daily routines and personal health. The combination of blockchain, machine learning, deep learning, and wearable Internet of Things (IoT) is creating a dynamic environment that presents both opportunities and difficulties with urgent worries about data security and privacy. Therefore, this work presents a hyperledger sawtooth-based secure distributed deep federated learning framework (SDDFL) for wearable IoT. A deep federated learning(FL) approach based on ANN, CNN, LSTM, and GRU is presented. The performance evaluation of the proposed approach has been done using Accuracy, RMSE, MAE, and MAPE. Locally, GRU performed consistently better as compared with others for different epochs with 99.28% for 50, 99.88% for 75, and 99.88% for 100 epochs. CNN-FL performed greatly with the highest test accuracy 97.78% for 4 and 98.22% for 6 nodes. GRU-FL has the highest train accuracy 99.91% for 4 nodes and for 6 nodes is also 99.91%. The results show that the proposed approach has high accuracy as compared with existing works. Further, the performance of the blockchain network has been evaluated using Throughput, Latency, and success rate. We get the maximum throughput of 48.8 and 50 for both write and read operations at 50TPS; and a success rate of 100 at 50 TPS and 10 TPS. The latency for open and query operations on the blockchain, showcasing a minimum of 0.21s and an average latency of 0.02s for read at 50TPS.