mlsanityData to 14 Oct 2026

A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data

arXiv 2309.01275 ↗First posted 3 Sep 2023cs.LG math.PRcited by 14

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Abstract (arXiv)

In this paper, we investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data, there by preserving data privacy. In particular, we compare two strategies within this paradigm: Federated Averaging (FedAvg) and Personalized Federated Averaging (Per-FedAvg), focusing on their performance with Non-Identically and Independently Distributed (Non-IID) data. Our analysis shows that the level of data heterogeneity, modeled using a Dirichlet distribution, significantly affects the performance of both strategies, with Per-FedAvg showing superior robustness in conditions of high heterogeneity. Our results provide insights into the development of more effective and efficient machine learning strategies in a decentralized setting.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 3 Sep 2023.

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11 of 18 references.

References · 11

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  6. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  7. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  8. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 166
  9. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  10. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 109
  11. 2023Federated Learning for Water Consumption Forecasting in Smart Citiescited by 7

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Citing papers per year: 2024: 4; 2025: 5; 2026: 5.

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  1. 2024Exploring Vacant Classes in Label-Skewed Federated Learningcited by 10
  2. 2024FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learningcited by 4
  3. 2025A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProxcited by 4
  4. 2024Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systemscited by 3
  5. 2024Effectiveness of Distributed Gradient Descent with Local Steps for Overparameterized Modelscited by 1
  6. 2026FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projectioncited by 1
  7. 2026Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedbackcited by 1
  8. 2025Centralized vs. Federated Learning for Educational Data Mining: A Comparative Study on Student Performance Prediction with SAEB Microdatacited by 0
  9. 2025Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Predictioncited by 0
  10. 2025Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systemscited by 0
  11. 2025Local Performance vs. Out-of-Distribution Generalization: An Empirical Analysis of Personalized Federated Learning in Heterogeneous Data Environmentscited by 0
  12. 2026FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learningcited by 0
2 more
  1. 2026FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopycited by 0
  2. 2026Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systemscited by 0