mlsanityData to 7 Oct 2026

Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models

arXiv 2302.04464 ↗First posted 9 Feb 2023cs.LG cs.AI cs.DCcited by 10

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Abstract (arXiv)

Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in terms of fairness and computational efficiency due to the rising heterogeneity of edges, and thus usually results in sub-optimal performance in recent state-of-the-art (SOTA) solutions. In this paper, we propose a Customized Federated Learning (CFL) system to eliminate FL heterogeneity from multiple dimensions. Specifically, CFL tailors personalized models from the specially designed global model for each client jointly guided by an online trained model-search helper and a novel aggregation algorithm. Extensive experiments demonstrate that CFL has full-stack advantages for both FL training and edge reasoning and significantly improves the SOTA performance w.r.t. model accuracy (up to 7.2% in the non-heterogeneous environment and up to 21.8% in the heterogeneous environment), efficiency, and FL fairness.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 41 of its 73 references that are in the map. First posted on arXiv 9 Feb 2023.

Builds on

41 of 73 references.

References · 41

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29 more
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Cited by year

Citing papers per year: 2023: 4; 2024: 5; 2025: 1.

10 papers.

  1. 2023SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detectioncited by 28
  2. 2024Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospectscited by 12
  3. 2023SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalizationcited by 9
  4. 2023GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learningcited by 8
  5. 2023Attribute-Aware Representation Rectification for Generalized Zero-Shot Learningcited by 6
  6. 2024TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learningcited by 6
  7. 2024On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Surveycited by 5
  8. 2024Dual Expert Distillation Network for Generalized Zero-Shot Learningcited by 4
  9. 2024Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learningcited by 3
  10. 2025FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Datacited by 0