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IP-FL: Incentivized and Personalized Federated Learning

Ahmad Faraz Khan, Xinran Wang, Qi Le, Zain ul Abdeen, Azal Ahmad Khan, Haider Ali, Ming Jin, Jie Ding, Ali R. Butt, Ali Anwar

TL;DR

IP-FL addresses the limitation of traditional incentive mechanisms in federated learning by unifying clustering-based personalization with a token-based incentive system. The framework enables clients to bid for cluster membership and uses Shapley-value approximations to quantify contributions, driving accurate, client-driven clustering and higher PMA. Theoretical convergence guarantees and extensive experiments show IP-FL yields 8–45% test-accuracy gains, 3–38% PMA improvements, and 31–100% higher participation compared to baselines, while also enabling effective personalization for unseen clients. This approach meaningfully enhances participation, personalization quality, and robustness under data heterogeneity, with practical implications for deploying personalized federated learning in privacy-preserving settings.

Abstract

Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personalization with multiple cluster-level models and the non-monetary incentives such as personalized model appeal for clients. In this paper, we first propose to treat incentivization and personalization as interrelated challenges and solve them with an incentive mechanism that fosters personalized learning. Additionally, current methods depend on an aggregator for client clustering, which is limited by a lack of access to clients' confidential information due to privacy constraints, leading to inaccurate clustering. To overcome this, we propose direct client involvement, allowing clients to indicate their cluster membership preferences based on data distribution and incentive-driven feedback. Our approach enhances the personalized model appeal for self-aware clients with high-quality data leading to their active and consistent participation. Our evaluation demonstrates significant improvements in test accuracy (8-45%), personalized model appeal (3-38%), and participation rates (31-100%) over existing FL models, including those addressing data heterogeneity and personalization.

IP-FL: Incentivized and Personalized Federated Learning

TL;DR

IP-FL addresses the limitation of traditional incentive mechanisms in federated learning by unifying clustering-based personalization with a token-based incentive system. The framework enables clients to bid for cluster membership and uses Shapley-value approximations to quantify contributions, driving accurate, client-driven clustering and higher PMA. Theoretical convergence guarantees and extensive experiments show IP-FL yields 8–45% test-accuracy gains, 3–38% PMA improvements, and 31–100% higher participation compared to baselines, while also enabling effective personalization for unseen clients. This approach meaningfully enhances participation, personalization quality, and robustness under data heterogeneity, with practical implications for deploying personalized federated learning in privacy-preserving settings.

Abstract

Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personalization with multiple cluster-level models and the non-monetary incentives such as personalized model appeal for clients. In this paper, we first propose to treat incentivization and personalization as interrelated challenges and solve them with an incentive mechanism that fosters personalized learning. Additionally, current methods depend on an aggregator for client clustering, which is limited by a lack of access to clients' confidential information due to privacy constraints, leading to inaccurate clustering. To overcome this, we propose direct client involvement, allowing clients to indicate their cluster membership preferences based on data distribution and incentive-driven feedback. Our approach enhances the personalized model appeal for self-aware clients with high-quality data leading to their active and consistent participation. Our evaluation demonstrates significant improvements in test accuracy (8-45%), personalized model appeal (3-38%), and participation rates (31-100%) over existing FL models, including those addressing data heterogeneity and personalization.
Paper Structure (37 sections, 5 theorems, 58 equations, 9 figures, 11 tables, 3 algorithms)

This paper contains 37 sections, 5 theorems, 58 equations, 9 figures, 11 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Related Work
  4. Problem Formulation
  5. Proposed Methodology
  6. Profiler
  7. Token Manager
  8. Scheduler
  9. Convergence Analysis of the IP-FL
  10. Experimental Study
  11. Experimental Setup
  12. Focus of Experimental Study
  13. Test Accuracy Performance Study
  14. Experimental Study of Opt-outs and PMA
  15. Conclusion
  16. ...and 22 more sections

Key Result

Proposition 1

Let $\{\mathcal{C}_k\}_{k=1}^K$ represent the set of clusters formed by IP-FL algorithm, where each cluster $\mathcal{C}_k$ contains clients $\{i_1, \ldots, i_N\}$ with their respective data distributions $\{D_{i_1}, \ldots, D_{i_N}\}$. Cluster will converge and within each cluster $\mathcal{C}_k$,

Figures (9)

  • Figure 1: IP-FL design
  • Figure 2: Convergence Speed: IP-FL vs. other pFL algorithms
  • Figure 3: CDF of clients’ PMA for different datasets and methods
  • Figure 4: IP-FL and FedSoft with Synthetic CIFAR10 data
  • Figure 5: IP-FL with and without incentive (I/NI)
  • ...and 4 more figures

Theorems & Definitions (10)

  • Proposition 1
  • Remark 2
  • Theorem 3
  • Theorem 4
  • Remark 5
  • Theorem 6: Convergence of IP-FL Algorithm
  • Remark 7: Intuitions
  • Remark 8: Normalized counterpart
  • Corollary 8.1
  • proof