Abstract
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.
Abstract (translated)
联邦学习（FL）用于分布式场景下训练人工智能（AI）模型，同时确保用户的隐私安全。在联邦学习中，服务器通常不会接触到用户的数据。这种概念使得AI的训练过程在数据隐私方面变得更加高效。然而，在模型性能方面，联邦AI模型可能无法完全满足AI用户的需求和期望。此外，AI用户有着广泛的多样化需求，难以一一满足。这些问题可以通过AI模型优化、微调或个性化来实现最佳模型性能。 为了应对模型优化的挑战，我们提出了基于参考模型的联邦学习方法来进行最优微调，这种方法可以克服每一轮中的灾难性遗忘问题。该方法源自贝叶斯参数高效迁移学习，包括一个最优近似项，并通过使用包含先前模型参数的参考模型来解决每轮中出现的灾难性遗忘问题。因此，这种技术不仅实现了高模型性能，还降低了计算成本。
URL
https://arxiv.org/abs/2506.23210
