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FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification

Xin Xu, Binchang Ma, Zhixi Yu, Wei Liu

TL;DR

A novel federated learning framework, Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS), comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection).

Abstract

The application of federated domain generalization in person re-identification (FedDG-ReID) aims to enhance the model's generalization ability in unseen domains while protecting client data privacy. However, existing mainstream methods typically rely on global feature representations and simple averaging operations for model aggregation, leading to two limitations in domain generalization: (1) Using only global features makes it difficult to capture subtle, domain-invariant local details (such as accessories or textures); (2) Uniform parameter averaging treats all clients as equivalent, ignoring their differences in robust feature extraction capabilities, thereby diluting the contributions of high quality clients. To address these issues, we propose a novel federated learning framework, Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS), comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection).

FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification

TL;DR

A novel federated learning framework, Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS), comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection).

Abstract

The application of federated domain generalization in person re-identification (FedDG-ReID) aims to enhance the model's generalization ability in unseen domains while protecting client data privacy. However, existing mainstream methods typically rely on global feature representations and simple averaging operations for model aggregation, leading to two limitations in domain generalization: (1) Using only global features makes it difficult to capture subtle, domain-invariant local details (such as accessories or textures); (2) Uniform parameter averaging treats all clients as equivalent, ignoring their differences in robust feature extraction capabilities, thereby diluting the contributions of high quality clients. To address these issues, we propose a novel federated learning framework, Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS), comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection).
Paper Structure (15 sections, 7 equations, 4 figures, 3 tables, 1 algorithm)

This paper contains 15 sections, 7 equations, 4 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Domain Generalization
  4. Federated Learning
  5. Methodology
  6. Preliminaries
  7. Extraction of Robust Knowledge
  8. Federated Aggregation with Robust Knowledge Selection
  9. Experiments
  10. Experimental Setup
  11. Comparison to State-of-the-Arts
  12. Ablation Study
  13. Visualization
  14. Conclusion
  15. Acknowledgments

Figures (4)

  • Figure 1: Core challenges in FedDG-ReID: (a) Ignoring key local details that are invariant across domains; (b) Aggregation methods based on averaging dilute the ability of clients that are good at extracting cross-domain invariant features, thereby suppressing overall generalization ability.
  • Figure 2: Architectural diagram of FedARKS. Our method employs local branch training, in which each client fuses features in a dual-branch network. This figure illustrates the detailed process of local feature fusion and parameter aggregation, using client 1 as an example (other clients are shown in the background). After local training is complete, the server dynamically calibrates the aggregation weights of each client based on feature difference vectors and updates the global model in the next round of communication to improve cross-domain generalization capabilities.
  • Figure 3: Dynamic Attention Distribution Heatmap Comparison Across Diverse Scenarios.
  • Figure 4: Dynamic client weight distribution across 40 training epochs in FedARKS.