{"type":"ScholarlyArticle","title":"Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning","authors":[{"name":"Weihang Chen","slug":"weihang-chen","given":"Weihang","family":"Chen","affiliation":""},{"name":"Cheng Yang","slug":"cheng-yang","given":"Cheng","family":"Yang","affiliation":""},{"name":"Jie Ren","slug":"jie-ren","given":"Jie","family":"Ren","affiliation":""},{"name":"Zhiqiang Li","slug":"zhiqiang-li","given":"Zhiqiang","family":"Li","affiliation":""},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":""}],"abstract":"Real-life deployment of federated Learning (FL) often faces non-IID data, which leads to poor accuracy and slow convergence. Personalized FL (pFL) tackles these issues by tailoring local models to individual data sources and using weighted aggregation methods for client-specific learning. However, existing pFL methods often fail to provide each local model with global knowledge on demand while maintaining low computational overhead.  Additionally, local models tend to over-personalize their data during the training process, potentially dropping previously acquired global information. We propose FLAYER, a novel layer-wise learning method for pFL that optimizes local model personalization performance. FLAYER considers the different roles and learning abilities of neural network layers of individual local models.  It incorporates global information for each local model as needed to initialize the local model cost-effectively. It then dynamically adjusts learning rates for each layer during local training, optimizing the personalized learning process for each local model while preserving global knowledge. Additionally, to enhance global representation in pFL, FLAYER selectively uploads parameters for global aggregation in a layer-wise manner. We evaluate FLAYER on four representative datasets in computer vision and natural language processing domains. Compared to eight state-of-the-art pFL methods, FLAYER improves the inference accuracy, on average, by  5.20% (up to 14.29%). Code is available at https://github.com/lancasterJie/FLAYER/.","doi":"10.24963/ijcai.2025/541","url":"https://ijcai.org/proceedings/2025/541","pdf":"https://ijcai.org/proceedings/2025/0541.pdf","markdown":"https://ijcai.org/proceedings/2025/541.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/541","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":541,"pages":{"first":4860,"last":4868},"keywords":["Machine Learning: ML: Federated learning","Machine Learning: ML: Optimization"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}