FedPURIN: Programmed Update and Reduced INformation for Sparse Personalized Federated Learning

• URL: http://arxiv.org/abs/2510.16065v1
• Date: Fri, 17 Oct 2025 03:54:43 GMT
• Title: FedPURIN: Programmed Update and Reduced INformation for Sparse Personalized Federated Learning
• Authors: Lunchen Xie, Zehua He, Qingjiang Shi,
• Abstract summary: We propose Federated Learning with Programmed Update and Reduced INformation (FedPURIN)<n>FedPURIN strategically identifies critical parameters for transmission through an integer programming formulation.<n>It is integrated into a sparse aggregation scheme, achieving a significant communication reduction while preserving the efficacy.
• Score: 25.55616766154517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized Federated Learning (PFL) has emerged as a critical research frontier addressing data heterogeneity issue across distributed clients. Novel model architectures and collaboration mechanisms are engineered to accommodate statistical disparities while producing client-specific models. Parameter decoupling represents a promising paradigm for maintaining model performance in PFL frameworks. However, the communication efficiency of many existing methods remains suboptimal, sustaining substantial communication burdens that impede practical deployment. To bridge this gap, we propose Federated Learning with Programmed Update and Reduced INformation (FedPURIN), a novel framework that strategically identifies critical parameters for transmission through an integer programming formulation. This mathematically grounded strategy is seamlessly integrated into a sparse aggregation scheme, achieving a significant communication reduction while preserving the efficacy. Comprehensive evaluations on standard image classification benchmarks under varied non-IID conditions demonstrate competitive performance relative to state-of-the-art methods, coupled with quantifiable communication reduction through sparse aggregation. The framework establishes a new paradigm for communication-efficient PFL, particularly advantageous for edge intelligence systems operating with heterogeneous data sources.

Related papers

• Implicit Federated In-context Learning For Task-Specific LLM Fine-Tuning [10.042856500868805]
  We propose the Implicit Federated In-Context Learning (IFed-ICL) framework.<n>IFed-ICL draws inspiration from federated learning to establish a novel distributed collaborative paradigm.<n>Compared to traditional methods, IFed-ICL avoids the extensive parameter updates required by conventional fine-tuning methods.
  arXiv  Detail & Related papers  (2025-11-10T06:34:29Z)
• CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks [51.43780477302533]
  Contribution-Oriented PFL (CO-PFL) is a novel algorithm that dynamically estimates each client's contribution for global aggregation.<n>CO-PFL consistently surpasses state-of-the-art methods in robustness in personalization accuracy, robustness, scalability and convergence stability.
  arXiv  Detail & Related papers  (2025-10-23T05:10:06Z)
• Intrinsic Training Signals for Federated Learning Aggregation [13.540945877050525]
  Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy.<n>This work demonstrates that effective model merging can be achieved solely through existing training signals.
  arXiv  Detail & Related papers  (2025-07-09T13:03:23Z)
• Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation [22.454292668849035]
  Federated Task-agnostic Low-rank Residual Adaptation (Fed-TaLoRA)<n>We develop a novel residual weight update mechanism that ensures accurate knowledge consolidation with minimal overhead.<n>Our methodological innovations are attributed to three key strategies: task-agnostic adaptation, post-aggregation model calibration, and strategic placement of LoRA modules.
  arXiv  Detail & Related papers  (2025-05-18T09:19:13Z)
• 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)
• FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning [9.084674176224109]
  Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. We introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information.
  arXiv  Detail & Related papers  (2024-10-17T22:47:19Z)
• SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead [75.87007729801304]
  SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead.<n>To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters.<n>Global thresholds are used to update model parameters by extracting aggregated parameter importance.
  arXiv  Detail & Related papers  (2024-06-01T13:10:35Z)
• Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs [12.023861154677205]
  This paper introduces a novel framework for hierarchical federated learning (HFL) over multi-hop clustering-based VANET. The proposed method utilizes a weighted combination of the average relative speed and cosine similarity of FL model parameters as a clustering metric. Through extensive simulations, the proposed hierarchical federated learning over clustered VANET has been demonstrated to improve accuracy and convergence time significantly.
  arXiv  Detail & Related papers  (2024-01-18T20:05:34Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• 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)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• Tensor Decomposition based Personalized Federated Learning [12.420951968273574]
  Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data. Due to FL's frequent communication and average aggregation strategy, they experience challenges scaling to statistical diversity data and large-scale models. We propose a personalized FL framework, named Decomposition based Personalized learning (TDPFed), in which we design a novel tensorized local model with tensorized linear layers and convolutional layers to reduce the communication cost.
  arXiv  Detail & Related papers  (2022-08-27T08:09:14Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.