mlsanityData to 7 Oct 2026

fluke: Federated Learning Utility frameworK for Experimentation and research

arXiv 2412.15728 ↗First posted 20 Dec 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Since its inception in 2016, Federated Learning (FL) has been gaining tremendous popularity in the machine learning community. Several frameworks have been proposed to facilitate the development of FL algorithms, but researchers often resort to implementing their algorithms from scratch, including all baselines and experiments. This is because existing frameworks are not flexible enough to support their needs or the learning curve to extend them is too steep. In this paper, we present fluke, a Python package designed to simplify the development of new FL algorithms. fluke is specifically designed for prototyping purposes and is meant for researchers or practitioners focusing on the learning components of a federated system. fluke is open-source, and it can be either used out of the box or extended with new algorithms with minimal overhead.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 50 references that are in the map. First posted on arXiv 20 Dec 2024.

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References · 22

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10 more
  1. 2020Adaptive Personalized Federated Learningcited by 353
  2. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
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  5. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
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  8. 2019DenseNet Models for Tiny ImageNet Classificationcited by 17
  9. 2024TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clientscited by 7
  10. 2018CINIC-10 is not ImageNet or CIFAR-10cited by 5

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