# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 6 Conclusions  
The emergence of federated learning, breaks the barriers of joint training models between different parties, and also provides protection for sensitive data. In the client-server framework of federated learning, the server does not access the clients' original data. However, the clients still face the risk of privacy violation. Differential privacy is a kind of privacy protection technology based on strict mathematical theory. Previous studies have applied differential privacy to federated learning, to enhance privacy protection. Different clients have different privacy requirements. Previous DP-based approaches usually set the same privacy budget for all clients and cannot provide personalized privacy protection. In this paper, under the basic framework of LDP-based federated learning, we propose a weighted average method and a probability-based selection method for model aggregation, to obtain the trade-off between privacy and accuracy. The basic idea of the proposed methods is to assign different weights to different clients according to the privacy budgets set by clients. We consider two cases, where clients' training data are IID or non-IID. Different model perturbation methods are applied to satisfy the constraint of LDP. The experiments show that the performances of the proposed aggregation methods are better  
Figure 6: Simulation results on Fashion-MNIST data. Clients’ training data are non-independent and identically distributed. (**a**) The non-personalized scenario. (**b**) The personalized scenario under the first privacy setting. (**c**) The personalized scenario under the second privacy setting.. We nominate \(\{5,5,5,10,10,10,15,15,15\}\) as \(\{L,M,H\}\) (i.e., low, middle, high), and nominate \(\{5,5,5,5,5,15,15,15,15,15\}\) as \(\{L,H\}\) (i.e., low, high). Figure 7: Simulation results on forest cover-types data. Clients’ training data are non-independent and identically distributed. (**a**) The non-personalized scenario. (**b**) The personalized scenario under the first privacy setting. (**c**) The personalized scenario under the second privacy setting. We nominate \(\{1,1,1,5,5,5,5,10,10,10\}\) as \(\{L,M,H\}\) (i.e., low, middle, high), and nominate \(\{1,1,1,1,1,10,10,10,10,10,10\}\) as \(\{L,H\}\) (i.e., low, high). than that of the arithmetic average method. In the future, we plan to further explore the diversity of privacy requirement and design better model perturbation methods. We also plan to investigate other solutions for federated learning with non-IID data. Conceptualization, X.W. and L.X. ; data curation, X.W. ; formal analysis, X.W. ; funding acquisition, L.X. and L.Z. ; investigation, X.W. ; methodology, X.W., L.X. and L.Z. ; project administration, L.X. and L.Z. ; resources, L.X. ; software, X.W. ; supervision, L.X. and L.Z. ; validation, X.W. ; visualization, X.W. ; writing--original draft, X.W. ; writing--review and editing, L.X. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Natural Science Foundation of China under Grant No. 62272039 and in part by the Beijing Natural Science Foundation under Grant No. M21035. The APC was funded by the National Natural Science Foundation of China under Grant No. 62272039. MNIST dataset ([[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/))), Fashion-MNIST dataset ([[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist))), Forest Cover-types dataset ([[https://archive.ics.uci.edu/ml/datasets/Covertype](https://archive.ics.uci.edu/ml/datasets/Covertype)]([https://archive.ics.uci.edu/ml/datasets/Covertype](https://archive.ics.uci.edu/ml/datasets/Covertype))). The authors declare no conflict of interest.