标题： FedRef：使用参考模型的通信高效贝叶斯微调 显示英文标题

标题： FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model

摘要： 联邦学习（FL）用于分布式场景，在确保用户隐私的同时训练人工智能（AI）模型。 在联邦学习场景中，服务器通常不会了解用户的数据。 这种概念使得人工智能训练过程在数据隐私方面更加高效。 然而，就模型性能而言，联邦AI模型可能无法充分满足人工智能用户的期望。 此外，人工智能用户有各种不同的需求。 要满足所有用户的需求并不容易。 这类问题可以通过人工智能模型优化、微调或个性化来解决，以实现最佳的模型性能。 为了解决模型优化挑战，我们提出了基于参考模型的联邦学习，以实现最佳微调，克服每轮中的灾难性遗忘。 该方法源自贝叶斯参数高效的迁移学习，包括一个最优邻近项，并通过利用包含先前模型参数的参考模型，能够在每轮中克服灾难性遗忘问题。 因此，该方法实现了高模型性能和低计算成本。 显示英文摘要

摘要： Federated learning(FL) is used for distributed scenarios to train artificial intelligence(AI) models while ensuring users' privacy. In federated learning scenario, the server generally never knows about users' data. This type of concept makes the AI training process efficient in terms of data privacy. However, regarding model performance, federated AI models may not sufficiently satisfy AI users' expectations. Furthermore, AI users have a wide range of different needs. It is not easy to satisfy the whole users needs. These types of issues can be addressed through AI model optimization, fine-tuning, or personalization to achieve optimal model performance. To address model optimization challenges, we propose reference model-based federated learning for optimal fine-tuning, which overcomes catastrophic forgetting in each round. This method is derived from Bayesian parameter-efficient transfer learning, which includes an optimal proximal term and enables overcoming the catastrophic forgetting issue in each round by utilizing a reference model that incorporates previous model parameters. As a result, this method achieves both high model performance and low computing cost.