mlsanityData to 14 Oct 2026

IP-FL: Incentivized and Personalized Federated Learning

arXiv 2304.07514 ↗First posted 15 Apr 2023cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 15 Apr 2023.

Builds on

21 of 56 references.

References · 21

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  6. 2021Towards Personalized Federated Learningcited by 464
  7. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  8. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  9. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  10. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  11. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  12. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
9 more
  1. 2019Reliable Federated Learning for Mobile Networkscited by 92
  2. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 74
  3. 2021Towards Fairness-Aware Federated Learningcited by 58
  4. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  5. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  6. 2021Personalized Federated Learning with Contextualized Generalizationcited by 23
  7. 2022Self-Aware Personalized Federated Learningcited by 16
  8. 2022Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoderscited by 4
  9. 2020Meta Clustering for Collaborative Learningcited by 2

Built on by

Not cited yet.