FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint Verification

• URL: http://arxiv.org/abs/2503.04837v1
• Date: Wed, 05 Mar 2025 14:49:42 GMT
• Title: FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint Verification
• Authors: Ziyuan Yang, Yingyu Chen, Chengrui Gao, Andrew Beng Jin Teoh, Bob Zhang, Yi Zhang,
• Abstract summary: Federated learning(FL) offers a privacy-preserving distributed learning paradigm for palmprint verification.<n>This paper defines and evaluates two practical scenarios: closed-set and open-set verification.<n>We propose FedPalm, a unified FL framework that balances local adaptability with global generalization.
• Score: 26.788980856429024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current deep learning (DL)-based palmprint verification models rely on centralized training with large datasets, which raises significant privacy concerns due to biometric data's sensitive and immutable nature. Federated learning~(FL), a privacy-preserving distributed learning paradigm, offers a compelling alternative by enabling collaborative model training without the need for data sharing. However, FL-based palmprint verification faces critical challenges, including data heterogeneity from diverse identities and the absence of standardized evaluation benchmarks. This paper addresses these gaps by establishing a comprehensive benchmark for FL-based palmprint verification, which explicitly defines and evaluates two practical scenarios: closed-set and open-set verification. We propose FedPalm, a unified FL framework that balances local adaptability with global generalization. Each client trains a personalized textural expert tailored to local data and collaboratively contributes to a shared global textural expert for extracting generalized features. To further enhance verification performance, we introduce a Textural Expert Interaction Module that dynamically routes textural features among experts to generate refined side textural features. Learnable parameters are employed to model relationships between original and side features, fostering cross-texture-expert interaction and improving feature discrimination. Extensive experiments validate the effectiveness of FedPalm, demonstrating robust performance across both scenarios and providing a promising foundation for advancing FL-based palmprint verification research.

Related papers

• Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation [3.499870393443268]
  Federated learning (FL) has recently garnered attention as a data-decentralized training framework. We propose a benchmark process to establish an FL benchmark with controllable semantic heterogeneity across clients. As a proof of concept, we first construct a federated PSG benchmark, demonstrating the efficacy of the existing PSG methods in an FL setting.
  arXiv  Detail & Related papers  (2024-12-11T08:10:46Z)
• 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)
• Federated Knowledge Graph Unlearning via Diffusion Model [5.373752180709173]
  Federated learning (FL) promotes the development and application of artificial intelligence technologies. In this paper, we propose FedDM, a novel framework tailored for machine unlearning in federated knowledge graphs.
  arXiv  Detail & Related papers  (2024-03-13T14:06:51Z)
• FLASH: Federated Learning Across Simultaneous Heterogeneities [54.80435317208111]
  FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm. It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities. It achieves substantial and consistent improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-13T20:04:39Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning [55.930403371398114]
  We propose a novel generative adversarial network (GAN) sharing and aggregation strategy for personalized learning (PFL) PFL-GAN addresses the client heterogeneity in different scenarios. More specially, we first learn the similarity among clients and then develop an weighted collaborative data aggregation. The empirical results through the rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.
  arXiv  Detail & Related papers  (2023-08-23T22:38:35Z)
• FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation [23.0771949978506]
  Existing skeleton-based action recognition methods typically follow a centralized learning paradigm, which can pose privacy concerns when exposing human-related videos. We introduce a novel Federated Skeleton-based Action Recognition (FSAR) paradigm, which enables the construction of a globally generalized model without accessing local sensitive data.
  arXiv  Detail & Related papers  (2023-06-19T16:18:14Z)
• FederatedTrust: A Solution for Trustworthy Federated Learning [3.202927443898192]
  The rapid expansion of the Internet of Things (IoT) has presented challenges for centralized Machine and Deep Learning (ML/DL) methods. To address concerns regarding data privacy, collaborative and privacy-preserving ML/DL techniques like Federated Learning (FL) have emerged.
  arXiv  Detail & Related papers  (2023-02-20T09:02:24Z)
• Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning [19.505644178449046]
  We propose a novel and general method named Federated Self-supervised Learning with Feature-correlation based Aggregation (FedFoA) Our insight is to utilize feature correlation to align the feature mappings and calibrate the local model updates across clients during their local training process. We prove that FedFoA is a model-agnostic training framework and can be easily compatible with state-of-the-art unsupervised FL methods.
  arXiv  Detail & Related papers  (2022-11-14T13:59:50Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)

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.