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

Hamza Reguieg, Mohammed El Hanjri, Mohamed El Kamili, Abdellatif Kobbane
9/3/2023
cs.LGmath.PR

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Abstract

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.

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Cite this paper

@article{reguieg2023comparative,
  title  = {A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data},
  author = {Hamza Reguieg and Mohammed El Hanjri and Mohamed El Kamili and Abdellatif Kobbane},
  year   = {2023},
  eprint = {2309.01275},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.01275v1}
}

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