CLMD:Detection and Prevention of Poisoning Attacks for Federated Learning in Maritime Communication Network
Chengzhuo Han, Tingting Yang, Xin Sun, Jiahong Ning · 2023
As maritime communication networks become more developed and widely used, an increasing number of end devices are being connected to these networks. Edge computing is an effective way to meet the realtime computing needs of these devices. However, with federated learning becoming more prevalent in edge computing decision-making, resource-constrained end devices are becoming more vulnerable to security threats. In particular, poisoning attacks are a significant security concern in the federated learning training process as local data is invisible to the outside world, enabling malicious participants to easily tamper with it. To address this issue, this paper proposes a Common Layer Mean Detection method for poisoning attacks that reduces their impact on the accuracy and resource consumption of the federated model while ensuring its security. The proposed method identifies poisoning attacks by comparing the mean distribution differences between attackers and honest clients in the common layer. Aggregation weights are then set based on the detection results to eliminate the impact of spurious parameters of malicious participants on the overall model. The effectiveness of this approach is demonstrated by comparing it with related schemes in terms of security, communication overhead, and computational overhead. Overall, the proposed method is shown to be both secure and efficient, making it a valuable addition to the field of federated learning for maritime communication networks.