mlsanityData to 14 Oct 2026

DistFL: Distribution-aware Federated Learning for Mobile Scenarios

arXiv 2110.11619 ↗First posted 22 Oct 2021cs.LG cs.DCcited by 2

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Abstract (arXiv)

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-preserving machine learning for mobile clients. While traditional FL has demonstrated its superiority, it ignores the non-iid (independently identically distributed) situation, which widely exists in mobile scenarios. Failing to handle non-iid situations could cause problems such as performance decreasing and possible attacks. Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models. However, previous techniques overlook the root causes for the "symptoms": blindly aggregating models with the non-iid distributions. In this paper, we try to fundamentally address the issue by decomposing the overall non-iid situation into several iid clusters and conducting aggregation in each cluster. Specifically, we propose DistFL, a novel framework to achieve automated and accurate Distribution-aware Federated Learning in a cost-efficient way. DistFL achieves clustering via extracting and comparing the textitdistribution knowledge from the uploaded models. With this framework, we are able to generate multiple personalized models with distinctive distributions and assign them to the corresponding clients. Extensive experiments on mobile scenarios with popular model architectures have demonstrated the effectiveness of DistFL.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 39 of its 63 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

39 of 63 references.

References · 39

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27 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.

2 papers.

  1. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  2. 2025PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraintscited by 0