Secure Multi-Task Federated Panoptic Perception Algorithm for Connected Autonomous Vehicles

HUANG Xiaoge; CHEN Ming; TANG Yi; LIANG Chengchao; CHEN Qianbin

doi:10.11999/JEIT250749

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HUANG Xiaoge, CHEN Ming, TANG Yi, LIANG Chengchao, CHEN Qianbin. Secure Multi-Task Federated Panoptic Perception Algorithm for Connected Autonomous Vehicles[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250749

Citation:

HUANG Xiaoge, CHEN Ming, TANG Yi, LIANG Chengchao, CHEN Qianbin. Secure Multi-Task Federated Panoptic Perception Algorithm for Connected Autonomous Vehicles[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250749

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Secure Multi-Task Federated Panoptic Perception Algorithm for Connected Autonomous Vehicles

doi: 10.11999/JEIT250749 cstr: 32379.14.JEIT250749

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: This work is supported by the National Natural Science Foundation of China (62371082), Guangxi Science and Technology Project (AB24010317), Science and Technology Project of Chongqing Education Commission (KJZD-K202400606), Natural Science Foundation of Chongqing (CSTB2023NSCQ-MSX0726, CSTB2023NSCQ-LZX0014)

Received Date: 2025-08-12
Accepted Date: 2026-04-12
Rev Recd Date: 2026-04-12

Available Online: 2026-04-30

Abstract

Abstract

With the rapid development of vehicular networks and deep learning, connected autonomous vehicles (CAV) are now capable of collecting image data from driving scenarios and leveraging Convolutional Neural Networks for feature extraction and processing, thereby enabling efficient perception of their surroundings. However, due to the inherent complexity of driving scenarios, single-task models struggle to address various perception demands. And the performance of deep learning models heavily relies on large-scale data, while the data collected by individual vehicles is insufficient for training models with generalization capabilities. Federated learning overcomes data silos by enabling CAV to upload local model gradients instead of raw data to a central server for aggregation, which can preserve data privacy. Therefore, we present a Secure Multi-Task Federated Panoptic Perception algorithm for vehicular network scenarios. Firstly, the panoptic perception model is constructed to allow CAV to execute multiple perception tasks simultaneously. Besides, a CAV selection strategy based on hybrid scoring is designed to select high-quality local models from vehicles. Finally, a global model aggregation scheme based on Shamir secret sharing is introduced to prevent data leakage in the event of server attacks or outages, which employs secret sharing during the aggregation process. Simulation results validate the effectiveness of the proposed algorithm.
- panoptic perception,
- deep learning,
- federated learning,
- secret sharing

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References(15)

References

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