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As edge computing complements the cloud to enable computational services right at the network edge, federated learning (FL) can also benefit from close-by edge computing infrastructure. However, most prior works on federated edge learning (FEL) mainly focus on one shared global model during the federated training in edge systems. In a real edge computing scenario, there may co-exist multiple various FL models that are owned by different entities and used by different applications. Simultaneously training these models competes both computing and networking resources in the shared edge system. Therefore, in this work, we consider a multi-model federated edge learning where multiple FEL models are being trained in the edge network and edge servers can act as either parameter servers or workers of these FEL models. We formulate a joint participant selection and learning scheduling problem, which is a non-linear mixed-integer program, aiming to minimize the total cost of all FEL models while satisfying the desired convergence rate of trained FEL models and the constrained edge resources. We then design several algorithms by decoupling the original problem into two or three sub-problems which can be solved respectively and iteratively. Extensive simulations with real-world training datasets and FEL models show that our proposed algorithms can efficiently reduce the average total cost of all FEL models in a multi-model FEL setting compared with existing algorithms.
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This content will become publicly available on May 12, 2026
Resource Optimized Split Federated Learning: A Reinforcement Learning and Optimization Approach
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.
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- PAR ID:
- 10627901
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-0887-6
- Page Range / eLocation ID:
- 1650 to 1655
- Format(s):
- Medium: X
- Location:
- Abu Dhabi, United Arab Emirates
- Sponsoring Org:
- National Science Foundation
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