FedPPA: Progressive Parameter Alignment for Personalized Federated Learning

• URL: http://arxiv.org/abs/2510.14698v1
• Date: Thu, 16 Oct 2025 14:03:05 GMT
• Title: FedPPA: Progressive Parameter Alignment for Personalized Federated Learning
• Authors: Maulidi Adi Prasetia, Muhamad Risqi U. Saputra, Guntur Dharma Putra,
• Abstract summary: Federated Learning (FL) is designed as a decentralized, privacy-preserving machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data.<n>In real-world scenarios, clients often have heterogeneous computational resources and hold non-independent and identically distributed data (non-IID), which poses significant challenges during training.<n>We propose Progressive Alignment (FedPPA), which progressively aligns the weights of common layers across clients with the global model's weights.<n>Experiments on three image classification datasets, including MNIST, FMNIST, and CIFAR-10, demonstrate that FedPPA consistently outperforms existing FL algorithms,
• Score: 0.9931624906346306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is designed as a decentralized, privacy-preserving machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data. In real-world scenarios, however, clients often have heterogeneous computational resources and hold non-independent and identically distributed data (non-IID), which poses significant challenges during training. Personalized Federated Learning (PFL) has emerged to address these issues by customizing models for each client based on their unique data distribution. Despite its potential, existing PFL approaches typically overlook the coexistence of model and data heterogeneity arising from clients with diverse computational capabilities. To overcome this limitation, we propose a novel method, called Progressive Parameter Alignment (FedPPA), which progressively aligns the weights of common layers across clients with the global model's weights. Our approach not only mitigates inconsistencies between global and local models during client updates, but also preserves client's local knowledge, thereby enhancing personalization robustness in non-IID settings. To further enhance the global model performance while retaining strong personalization, we also integrate entropy-based weighted averaging into the FedPPA framework. Experiments on three image classification datasets, including MNIST, FMNIST, and CIFAR-10, demonstrate that FedPPA consistently outperforms existing FL algorithms, achieving superior performance in personalized adaptation.

Related papers

• 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)
• Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients [59.52341877720199]
  We propose FedMosaic, a method that enables knowledge sharing across heterogeneous architectures without huge computational cost.<n>To mimic the real-world task diversity, we propose a multi-modal PFL benchmark spanning 40 distinct tasks with distribution shifts over time.<n>The empirical study shows that FedMosaic outperforms the state-of-the-art PFL methods.
  arXiv  Detail & Related papers  (2025-05-20T09:17:07Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients [13.98392319567057]
  Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis. Despite their wide adoption, existing FL and PFL works have yet to comprehensively address the class-imbalance issue. We propose FedReMa, an efficient PFL algorithm that can tackle class-imbalance by utilizing an adaptive inter-client co-learning approach.
  arXiv  Detail & Related papers  (2024-11-04T05:44:28Z)
• FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization [11.040916982022978]
  Federated Learning (FL) enables collaborative training of machine learning models on decentralized data. Data across clients often differs significantly due to class imbalance, feature distribution skew, sample size imbalance, and other phenomena. We propose a novel Bayesian PFL framework using bi-level optimization to tackle the data heterogeneity challenges.
  arXiv  Detail & Related papers  (2024-05-29T11:28:06Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach [37.90061908863874]
  Federated Learning aims to infer a shared model from private and decentralized data stored by multiple clients.<n>We introduce the PAC-PFL framework for PFL of probabilistic models.<n>Unlike previous PFL algorithms, PAC-PFL does not regularize all personalized models towards a single shared model.
  arXiv  Detail & Related papers  (2024-01-16T13:30:37Z)
• Confidence-aware Personalized Federated Learning via Variational Expectation Maximization [34.354154518009956]
  We present a novel framework for personalized Federated Learning (PFL) PFL is a distributed learning scheme to train a shared model across clients. We present a novel framework for PFL based on hierarchical modeling and variational inference.
  arXiv  Detail & Related papers  (2023-05-21T20:12:27Z)
• Efficient Personalized Federated Learning via Sparse Model-Adaptation [47.088124462925684]
  Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. We propose pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models. We show that pFedGate achieves superior global accuracy, individual accuracy and efficiency simultaneously over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T12:21:34Z)
• 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)
• Visual Prompt Based Personalized Federated Learning [83.04104655903846]
  We propose a novel PFL framework for image classification tasks, dubbed pFedPT, that leverages personalized visual prompts to implicitly represent local data distribution information of clients. Experiments on the CIFAR10 and CIFAR100 datasets show that pFedPT outperforms several state-of-the-art (SOTA) PFL algorithms by a large margin in various settings.
  arXiv  Detail & Related papers  (2023-03-15T15:02:15Z)
• Personalizing or Not: Dynamically Personalized Federated Learning with Incentives [37.42347737911428]
  We propose personalized federated learning (FL) for learning personalized models without sharing private data. We introduce the personalization rate, measured as the fraction of clients willing to train personalized models, into federated settings and propose DyPFL. This technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better.
  arXiv  Detail & Related papers  (2022-08-12T09:51:20Z)

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.