Automatic Configuration of Federated Learning Client in Graph Classification using Genetic Algorithms | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 1، فروردین 2024، صفحه 115-126 اصل مقاله (1.5 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.13688.2487 | ||
| نویسندگان | ||
| Mohammad Rezaei؛ Mohsen Rezvani* ؛ Morteza Zahedi | ||
| Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| چکیده | ||
| 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. 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. | ||
| کلیدواژهها | ||
| Federated Learning؛ Model Agnostic Meta-Learning (MAML)؛ Federated Learning on Graph | ||
| مراجع | ||
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