Federated Learning is a distributed machine learning environment, which ensures that clients complete collaborative training without sharing private data, only by exchanging parameters. However, the data does not satisfy the same distribution and the computing resources of clients are different, which brings challenges to the related research. To better solve the above heterogeneous problems, we designed a novel federated learning method. The local model consists of the pre-trained model as the backbone and fully connected layers as the head. The backbone can extract features for the head, and the embedding vector of classes is shared between clients to optimize the head so that the local model can perform better. By sharing the embedding vector of classes, instead of parameters based on gradient space, clients can better adapt to private data, and it is more efficient in the communication between the server and clients. To better protect privacy, we proposed a privacy-preserving hybrid method to add noise to the embedding vector of classes, which has less impact on the local model performance under the premise of satisfying differential privacy. We conduct a comprehensive evaluation with other federated learning methods on the self-built vehicle dataset under non-independent identically distributed(Non-IID)

翻译：联邦学习是一种分布式机器学习环境，它通过仅交换参数确保客户端在不共享私有数据的情况下完成协同训练。然而，数据不满足相同分布且客户端的计算资源各异，这为相关研究带来了挑战。为更好地解决上述异构性问题，我们设计了一种新型联邦学习方法。本地模型由作为骨干网络的预训练模型和作为头部的全连接层组成。骨干网络可为头部提取特征，而各类别的嵌入向量在客户端之间共享以优化头部，从而使本地模型表现更佳。通过共享类别的嵌入向量（而非基于梯度空间的参数），客户端能更好地适应私有数据，并且在服务器与客户端之间的通信效率更高。为加强隐私保护，我们提出了一种隐私保护混合方法，在满足差分隐私的前提下向类别嵌入向量添加噪声，从而减少对本地模型性能的影响。我们在自建的非独立同分布（Non-IID）车辆数据集上，与其他联邦学习方法进行了全面评估。