FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model

• URL: http://arxiv.org/abs/2506.23210v2
• Date: Wed, 16 Jul 2025 11:06:41 GMT
• Title: FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model
• Authors: Taehwan Yoon, Bongjun Choi,
• Abstract summary: Federated learning(FL) is used for distributed scenarios to train artificial intelligence(AI) models.<n>In federated learning scenario, the server generally never knows about users' data.<n>We propose reference model-based federated learning for optimal fine-tuning.
• Score: 1.5028783379278987
• License: http://creativecommons.org/licenses/by/4.0/
• 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 utilizes a reference model that incorporates previous model parameters. As a result, this method achieves both high model performance and clients' low computing cost.

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