A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for
  Dirichlet Distributed Heterogeneous Data
Hamza Reguieg Mohammed El Hanjri Mohamed El Kamili Abdellatif Kobbane
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.