A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data-Reference-Cited by-同舟云学术

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

Published:2023-10-26 Issue: Volume: Page:
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Container-title:2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM)
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Author:

Reguieg Hamza¹,Hanjri Mohammed El²,Kamili Mohamed El¹,Kobbane Abdellatif²

Affiliation:

1. Hassan II University in Casablanca,Higher School of Technology,Morocco

2. Mohammed V University in Rabat,ENSIAS,Morocco

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10322877/10322879/10322899.pdf?arnumber=10322899

Reference18 articles.

1. Federated learning based on dynamic regularization;acar;CoRR,2021

2. Model-Contrastive Federated Learning

3. Federated learning with label distribution skew via logits calibration;zhang,2022

4. Data-free knowledge distillation for heterogeneous federated learning;zhu;CoRR,2021

5. SCAFFOLD: stochastic controlled averaging for on-device federated learning;karimireddy;CoRR,2019

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1. Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. COMPARATIVE ANALYSIS OF FEDERATED MACHINE LEARNING ALGORITHMS;Scientific Journal of Astana IT University;2024-05-20

3. 2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning;Computer Modeling in Engineering & Sciences;2024

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