Personalized federated prototype learning in mixed heterogeneous data scenarios

• URL: http://arxiv.org/abs/2510.03726v1
• Date: Sat, 04 Oct 2025 08:08:32 GMT
• Title: Personalized federated prototype learning in mixed heterogeneous data scenarios
• Authors: Jiahao Zeng, Wolong Xing, Liangtao Shi, Xin Huang, Jialin Wang, Zhile Cao, Zhenkui Shi,
• Abstract summary: Federated learning has received significant attention for its ability to simultaneously protect customer privacy and leverage distributed data from multiple devices for model training.<n>We propose a new approach called PFPL in mixed heterogeneous scenarios.<n>The method provides richer domain knowledge and unbiased convergence targets by constructing personalized, unbiased prototypes for each client.
• Score: 8.36422671527418
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated learning has received significant attention for its ability to simultaneously protect customer privacy and leverage distributed data from multiple devices for model training. However, conventional approaches often focus on isolated heterogeneous scenarios, resulting in skewed feature distributions or label distributions. Meanwhile, data heterogeneity is actually a key factor in improving model performance. To address this issue, we propose a new approach called PFPL in mixed heterogeneous scenarios. The method provides richer domain knowledge and unbiased convergence targets by constructing personalized, unbiased prototypes for each client. Moreover, in the local update phase, we introduce consistent regularization to align local instances with their personalized prototypes, which significantly improves the convergence of the loss function. Experimental results on Digits and Office Caltech datasets validate the effectiveness of our approach and successfully reduce the communication cost.

Related papers

• Local Performance vs. Out-of-Distribution Generalization: An Empirical Analysis of Personalized Federated Learning in Heterogeneous Data Environments [3.186130813218338]
  This study involves a thorough evaluation of Federated Learning approaches, encompassing both their local performance and their generalization capabilities.<n>We propose and incorporate a modified approach of FedAvg, designated as Federated Learning with Individualized Updates (FLIU), extending the algorithm by a straightforward individualization step with an adaptive personalization factor.
  arXiv  Detail & Related papers  (2025-10-28T15:15:14Z)
• Heterogeneous Self-Supervised Acoustic Pre-Training with Local Constraints [64.15709757611369]
  We propose a new self-supervised pre-training approach to dealing with heterogeneous data.<n>The proposed approach can significantly improve the adaptivity of the self-supervised pre-trained model for the downstream supervised fine-tuning tasks.
  arXiv  Detail & Related papers  (2025-08-27T15:48:50Z)
• Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach [22.030687488408496]
  Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative learning.<n>We present HyperFedZero, a novel method that dynamically generates specialized models via a hypernetwork conditioned on distribution-aware embeddings.
  arXiv  Detail & Related papers  (2025-08-18T07:11:51Z)
• Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift [2.298932494750101]
  We show that inconsistencies in client-side training distributions substantially degrade the performance of federated learning models.<n>We propose a novel FL framework using a combination of pruning and regularisation of clients' training to improve the sparsity, redundancy, and robustness of neural connections.
  arXiv  Detail & Related papers  (2024-12-19T16:22:37Z)
• FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning [12.307490659840845]
  Federated Learning (FL) combines locally optimized models from various clients into a unified global model.<n>FL encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model.<n>We introduce an innovative dual-strategy approach designed to effectively resolve these issues.
  arXiv  Detail & Related papers  (2024-12-05T18:42:29Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization [35.48757125452761]
  Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices. A significant challenge of federated learning is data-level heterogeneity, i.e., skewed or long-tailed distribution of private data. We propose a novel Federated Prototype Rectification with Personalization which consists of two parts: Federated Personalization and Federated Prototype Rectification.
  arXiv  Detail & Related papers  (2024-08-15T06:26:46Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• FedGen: Generalizable Federated Learning for Sequential Data [8.784435748969806]
  In many real-world distributed settings, spurious correlations exist due to biases and data sampling issues. We present a generalizable federated learning framework called FedGen, which allows clients to identify and distinguish between spurious and invariant features. We show that FedGen results in models that achieve significantly better generalization and can outperform the accuracy of current federated learning approaches by over 24%.
  arXiv  Detail & Related papers  (2022-11-03T15:48:14Z)
• Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks [65.34113135080105]
  We show that not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for the FL participants. Our observations are intuitive. Our code is available at https://github.com/MMorafah/FL-SC-NIID.
  arXiv  Detail & Related papers  (2022-09-30T17:15:19Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.