Federated Learning is a distributed machine-learning environment that allows clients to learn collaboratively without sharing private data. This is accomplished by exchanging parameters. However, the differences in data distributions and computing resources among clients make related studies difficult. To address these heterogeneous problems, we propose a novel Federated Learning method. Our method utilizes a pre-trained model as the backbone of the local model, with fully connected layers comprising the head. The backbone extracts features for the head, and the embedding vector of classes is shared between clients to improve the head and enhance the performance of the local model. By sharing the embedding vector of classes instead of gradient-based parameters, clients can better adapt to private data, and communication between the server and clients is more effective. To protect privacy, we propose a privacy-preserving hybrid method that adds noise to the embedding vector of classes. This method has a minimal effect on the performance of the local model when differential privacy is met. We conduct a comprehensive evaluation of our approach on a self-built vehicle dataset, comparing it with other Federated Learning methods 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，允许多个客户端在不共享私有数据的情况下协同学习，这是通过参数交换实现的。然而，客户端之间数据分布与计算资源的差异给相关研究带来了困难。为解决这些异构性问题，我们提出了一种新颖的联邦学习方法。该方法利用预训练模型作为本地模型的主干网络（backbone），并由全连接层构成头部（head）。主干网络为头部提取特征，同时通过客户端之间共享类嵌入向量（embedding vector of classes）来改进头部，从而提升本地模型的性能。通过共享类嵌入向量而非基于梯度的参数，客户端能够更好地适应私有数据，且服务器与客户端之间的通信更加高效。为保护隐私，我们提出了一种隐私保护的混合方法，向类嵌入向量中添加噪声。当满足差分隐私（differential privacy）条件时，该方法对本地模型性能的影响极小。我们在自建车辆数据集上对方法进行了全面评估，并在非独立同分布（Non-IID）条件下与其他联邦学习方法进行了对比。