mlsanityData to 7 Oct 2026

Auxo: Efficient Federated Learning via Scalable Client Clustering

arXiv 2210.16656 ↗First posted 29 Oct 2022cs.LG cs.DCcited by 8

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Abstract (arXiv)

Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to a logically centralized server. However, beyond the heterogeneous device capacity, FL participants often exhibit differences in their data distributions, which are not independent and identically distributed (Non-IID). Many existing works present point solutions to address issues like slow convergence, low final accuracy, and bias in FL, all stemming from client heterogeneity. In this paper, we explore an additional layer of complexity to mitigate such heterogeneity by grouping clients with statistically similar data distributions (cohorts). We propose Auxo to gradually identify such cohorts in large-scale, low-availability, and resource-constrained FL populations. Auxo then adaptively determines how to train cohort-specific models in order to achieve better model performance and ensure resource efficiency. Our extensive evaluations show that, by identifying cohorts with smaller heterogeneity and performing efficient cohort-based training, Auxo boosts various existing FL solutions in terms of final accuracy (2.1% - 8.2%), convergence time (up to 2.2x), and model bias (4.8% - 53.8%).

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 41 of its 97 references that are in the map. First posted on arXiv 29 Oct 2022.

Builds on

41 of 97 references.

References · 41

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29 more
  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 1.

8 papers.

  1. 2024Federated Learning Clients Clustering with Adaptation to Data Driftscited by 3
  2. 2024LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learningcited by 2
  3. 2024FedTrans: Efficient Federated Learning via Multi-Model Transformationcited by 1
  4. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  5. 2025AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregatorcited by 1
  6. 2024Harnessing Increased Client Participation with Cohort-Parallel Federated Learningcited by 0
  7. 2025FLStore: Efficient Federated Learning Storage for non-training workloadscited by 0
  8. 2026Personalized Digital Health Modeling with Adaptive Support Userscited by 0