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A Federated Framework for Edge Computing Devices with Collaborative Fairness and Adversarial Robustness

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Abstract

Federated learning is a distributed machine learning framework for edge computing devices that provides several benefits, such as eliminating over-fitting and protecting privacy. However, the majority of federated learning paradigms have not taken fairness into account. Since the quality and quantity of the data held by each participant varies, their contributions are always diverse. In other words, the fact that all devices receive the same model as a reward, regardless of their various contributions, is unfair to those who contribute the most. In this work, we provide s-CFFL, a federated framework for edge computing devices that ingeniously combines the reputation mechanism with distributed selective stochastic gradient descent (DSSGD) to achieve collaborative fairness. In addition, we investigate the resistance of the framework against free-riders and several other common adversaries. We perform comprehensive trials comparing our framework to FedAvg, DSSGD, and other related approaches. The results indicate that our strategy strikes a compromise between models’ prediction accuracy and collaborative fairness while simultaneously boosting model robustness.

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Data Availability

The datasets generated and analyzed during the current study is not publicly available, but are available from the corresponding author on an reasonable request.

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Acknowledgements

Firstly, I would like to thank my best friend, Zijie Wang, who gave me a lot of moral support, encouragement and enlightenment when I was depressed, and secondly, I would like to thank my tutors and classmates who provided me with a lot of valuable and useful advice, and it was through discussions and exchanges with them that this manuscript was completed.

Funding

The study was supported by National Key Research and Development Program (2022YFB3305200).

Author information

Authors and Affiliations

  1. East China Normal University, Shanghai, China

    Hailin Yang, Yanhong Huang, Jianqi Shi & Yang Yang

  2. National Trusted Embedded Software Engineering Technology Research Center, Shanghai, China

    Yanhong Huang & Jianqi Shi

Authors
  1. Hailin Yang
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  2. Yanhong Huang
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  3. Jianqi Shi
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  4. Yang Yang
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Contributions

Hailin Yang came up with the original idea for the thesis, carried out a methodological feasibility study, then completed the design of the proposal with the help of Yanhong Huang and Jianqi Shi, followed by experimental design and data collection graphing. Hailin Yang wrote the main manuscript text, and Yanhong Huang, Jianqi Shi, and Yang Yang provided much help in reviewing and editing.

Corresponding author

Correspondence to Yanhong Huang.

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Appendix: Additional Experimental Results

Appendix: Additional Experimental Results

Table 5 Individual test accuracies [%] over MNIST dataset with re-scaling adversaries and value-inverting adversaries under I.I.D.
Full size table
Table 6 Average Test Accuracy[%] and Maximum Test Accuracy[%](in brackets) and fairness results on cifar10 dataset with 5 and 20 participants
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Fig. 11
figure 11

Individual performance for CIFAR10 10 participants under class numer imblance(CI) and data size imblance(DI) using Standalone framework and s-CFFL. The 2 rows correspond to {CI, DI} , the first 2 columns correspond to {Standalone, s-CFFL }.The third column plots the variation of the reputation values under the corresponding data distribution

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Fig. 12
figure 12

Individual performance for CIFAR10 20 participants under class numer imblance(CI) and data size imblance(DI) using Standalone framework and s-CFFL. The 2 rows correspond to {CI, DI} , the first 2 columns correspond to {Standalone, s-CFFL }.The third column plots the variation of the reputation values under the corresponding data distribution

Full size image

We examine two other types of untargeted attacks, namely value-inserting and re-scaling; value-inserting refers to randomly inverting the element-wise values of the gradients, and re-scaling refers to arbitrarily rescaling gradients. Table 5 shows the experimental data under re-scaling and value-inverting attack when the data is distributed independently and identically across participants, with 5 honest participants and 2 adversaries.

Compared with other similar methods, our proposed method is more robust under both attacks, can effectively distinguish between honest participants and adversaries, and facilitates the discharge of adversary interference earlier.

The experimental results on cifar10 dataset with 5 and 20 participants are shown as Table 6, Figs. 11 and 12. The experimental results complement the validity of our method. The reputation value reflects the quality of the data and shows a positive correlation with the final model accuracy. Based on the experimental data, we can see that our method achieves comparable or better results than CFFL in terms of fairness, which is much better than fedavg and DSSGD, which do not consider fairness, and q-FFL, which does consider equality.

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Yang, H., Huang, Y., Shi, J. et al. A Federated Framework for Edge Computing Devices with Collaborative Fairness and Adversarial Robustness. J Grid Computing 21, 36 (2023). https://doi.org/10.1007/s10723-023-09658-x

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