Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privacy, FL incorporates Secure Aggregation (SA) to prevent the server from obtaining individual gradients, thus effectively resisting GIAs. In this paper, we propose a stealthy label inference attack to bypass SA and recover individual clients’ private labels. Specifically, we conduct a theoretical analysis of label inference from the aggregated gradients that are exclusively obtained after implementing SA. The analysis results reveal that the inputs (embeddings) and outputs (logits) of the final fully connected layer (FCL) contribute to gradient disaggregation and label restoration. To preset the embeddings and logits of FCL, we craft a fishing model by solely modifying the parameters of a single batch normalization (BN) layer in the original model. Distributing client-specific fishing models, the server can derive the individual gradients regarding the bias of FCL by resolving a linear system with expected embeddings and the aggregated gradients as coefficients. Then the labels of each client can be precisely computed based on preset logits and gradients of FCL’s bias. Extensive experiments show that our attack achieves large-scale label recovery with 100% accuracy on various datasets and model architectures.

In edge control systems (ECSs), edge computing demands more local data processing power, while traditional industrial programmable logic controllers (PLCs) cannot meet this demand. Thus, edge intelligent controllers (EICs) have been developed, making their secure and reliable operation crucial. However, as EICs communicate sensitive information with resource-limited terminal devices (TDs), a low-cost, efficient authentication solution is urgently needed since it is challenging to implement traditional asymmetric cryptography on TDs. In this paper, we design a lightweight authentication scheme for ECSs using low-computational-cost hash functions and exclusive OR (XOR) operations; this scheme can achieve bidirectional anonymous authentication and key agreement between the EIC and TDs to protect the privacy of the devices. Through security analysis, we demonstrate that the authentication scheme can provide the necessary security features and resist major known attacks. Performance analysis and comparisons indicate that the proposed authentication scheme is effective and feasible for deployment in ECSs.