Resource Optimized Split Federated Learning: A Reinforcement Learning and Optimization Approach
Guizani, Maher Khan, Latif U. Ullah, Waseem Islam, Mohammad A.
Guizani, Maher
Khan, Latif U.
Ullah, Waseem
Islam, Mohammad A.
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
Federated learning (FL) offers many benefits, such as better privacy preservation and less communication overhead for scenarios with frequent data generation. In FL, local models are trained on end-devices and then migrated to the network edge or cloud for global aggregation. This aggregated model is shared back with end-devices to further improve their local models. This iterative process continues until convergence is achieved. Although FL has many merits, it has many challenges. The prominent one is computing resource constraints. End-devices typically have fewer computing resources and are unable to learn well the local models. Therefore, split FL (SFL) was introduced to address this problem. However, enabling SFL is also challenging due to wireless resource constraints and uncertainties. We formulate a joint end-devices computing resources optimization, task-offloading, and resource allocation problem for SFL at the network edge. Our problem formulation has a mixed-integer non-linear programming problem nature and hard to solve due to the presence of both binary and continuous variables. We propose a double deep Q-network (DDDQN) and optimization-based solution. Finally, we validate the proposed method using extensive simulation results.
Citation
M. Guizani, L. U. Khan, W. Ullah and M. A. Islam, "Resource Optimized Split Federated Learning: A Reinforcement Learning and Optimization Approach," 2025 International Wireless Communications and Mobile Computing (IWCMC), Abu Dhabi, United Arab Emirates, 2025, pp. 1650-1655, doi: 10.1109/IWCMC65282.2025.11059717.
Source
21st International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Conference
21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Keywords
Double deep Q-network, Federated learning, Split federated learning
Subjects
Source
21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Publisher
IEEE