AHFL-UAVs: adaptive loss isomorphism federated learning algorithm for UAV swarms

Fanliang Meng; Xiaoguang Nie; Juan Gu; Shengyu Jia; Ting Zhang; Yujian Li

doi:10.1117/12.3004027

11 October 2023 AHFL-UAVs: adaptive loss isomorphism federated learning algorithm for UAV swarms

Fanliang Meng, Xiaoguang Nie, Juan Gu, Shengyu Jia, Ting Zhang, Yujian Li

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001B (2023) https://doi.org/10.1117/12.3004027
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

UAV swarms have crucial applications in modern military, geological exploration, and 5G/6G communication fields. As communication nodes, drones frequently exchange wireless data with other drones, and data privacy protection is currently one of the most urgent research topics. Based on this, this article proposes an efficient isomorphic federated learning algorithm for unmanned aerial vehicle clusters. First, set the adaptive loss differential adaptive parameter, with the initial value set to a floating point number not greater than 0.01, then activate it with the Tanh function, participate in the training as part of the loss function during the training process, and optimize it by gradient descent. When the UAV node uploads the gradient to the wingman, sum the adaptive loss differential parameter with the gradient matrix as a disturbance term. Then, based on the dynamic confidence matrix, high-quality drones are selected to participate in the gradient security aggregation of drones, achieving the selection of high-quality drones. The built-in global neural network of the drone transmits shared parameters indiscriminately to each drone through broadcast to achieve updates to the shared parameters. The comparative experiments of our algorithm on the Fashion and Cifar10 datasets show that our algorithm has higher accuracy, with the highest accuracy improvement of 4.42% on the Mnist dataset and 8.22% on the Cifar10 dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fanliang Meng, Xiaoguang Nie, Juan Gu, Shengyu Jia, Ting Zhang, and Yujian Li "AHFL-UAVs: adaptive loss isomorphism federated learning algorithm for UAV swarms", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001B (11 October 2023); https://doi.org/10.1117/12.3004027

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KEYWORDS

Education and training

Machine learning

Unmanned aerial vehicles

Data privacy

Evolutionary algorithms

Computer security

Deep learning

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