mlsanityData to 7 Oct 2026

Improving Local Training in Federated Learning via Temperature Scaling

arXiv 2401.09986 ↗First posted 18 Jan 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

Federated learning is inherently hampered by data heterogeneity: non-i.i.d. training data over local clients. We propose a novel model training approach for federated learning, FLex&Chill, which exploits the Logit Chilling method. Through extensive evaluations, we demonstrate that, in the presence of non-i.i.d. data characteristics inherent in federated learning systems, this approach can expedite model convergence and improve inference accuracy. Quantitatively, from our experiments, we observe up to 6X improvement in the global federated learning model convergence time, and up to 3.37% improvement in inference accuracy.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

26 of 40 references.

References · 26

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14 more
  1. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
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  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
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  5. 2020Gradient Flow in Sparse Neural Networks and How Lottery Tickets Wincited by 77
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  7. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 55
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.

  1. 2024Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generationcited by 9
  2. 2025Private Federated Multiclass Post-hoc Calibrationcited by 0
  3. 2026Temperature Scaling Attack Disrupting Model Confidence in Federated Learningcited by 0
  4. 2026Deep Learning-based Assessment of the Relation Between the Third Molar and Mandibular Canal on Panoramic Radiographs using Local, Centralized, and Federated Learningcited by 0
  5. 2026Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learningcited by 0