Pradyumna Kumar Tripathy, Anurag Shrivastava, Varsha Agarwal, Devangkumar Umakant Shah, Chandra Sekhar Reddy L. and S.V. Akilandeeswari
This paper aims to provide the security and privacy for Byzantine clients from different types of attacks.
Abstract
Purpose
This paper aims to provide the security and privacy for Byzantine clients from different types of attacks.
Design/methodology/approach
In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.
Findings
By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped.
Originality/value
By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.
Details
Keywords
Tingting Tian, Hongjian Shi, Ruhui Ma and Yuan Liu
For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the…
Abstract
Purpose
For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.
Design/methodology/approach
This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.
Findings
While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.
Originality/value
By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.