Shahrood University of Technology Journal of AI and Data Mining 2322-5211 12 1 2024 01 01 Automatic Configuration of Federated Learning Client in Graph Classification using Genetic Algorithms 115 126 3163 10.22044/jadm.2024.13688.2487 EN Mohammad Rezaei Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran. Mohsen Rezvani Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran. Morteza Zahedi Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran. Journal Article 2023 11 05 With the increasing interconnectedness of communications and social networks, graph-based learning techniques offer valuable information extraction from data. Traditional centralized learning methods faced challenges, including data privacy violations and costly maintenance in a centralized environment. To address these, decentralized learning approaches like Federated Learning have emerged. This study explores the significant attention Federated Learning has gained in graph classification and investigates how Model Agnostic Meta-Learning (MAML) can improve its performance, especially concerning non-IID (Non-Independent Identically Distributed) data distributions.<br /><br />In real-world scenarios, deploying Federated Learning poses challenges, particularly in tuning client parameters and structures due to data isolation and diversity. To address this issue, this study proposes an innovative approach using Genetic Algorithms (GA) for automatic tuning of structures and parameters. By integrating GA with MAML-based clients in Federated Learning, various aspects, such as graph classification structure, learning rate, and optimization function type, can be automatically adjusted. This novel approach yields improved accuracy in decentralized learning at both the client and server levels. Federated Learning Model Agnostic Meta-Learning (MAML) Federated Learning on Graph https://jad.shahroodut.ac.ir/article_3163_d72167aa51d67a6110be38ef6106a8c4.pdf