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

• URL: http://arxiv.org/abs/2309.01275v1
• Date: Sun, 3 Sep 2023 21:33:15 GMT
• Title: A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data
• Authors: Hamza Reguieg, Mohammed El Hanjri, Mohamed El Kamili, Abdellatif Kobbane
• Abstract summary: We investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data. We compare two strategies within this paradigm: Federated Averaging (FedAvg) and Personalized Federated Averaging (Per-FedAvg) Our results provide insights into the development of more effective and efficient machine learning strategies in a decentralized setting.
• Score: 2.5507252967536522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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