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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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Incentive mechanism design for semi-asynchronous blockchain-based federated edge learning
Federated learning at edge systems not only mitigates privacy concerns by keeping data localized but also leverages edge computing resources to enable real-time AI inference and decision-making. In a blockchain-based federated learning framework over edge clouds, edge servers as clients can contribute private data or computing resources to the overall training or mining task for secure model aggregation. To overcome the impractical assumption that edge servers will voluntarily join training or mining, it is crucial to design an incentive mechanism that motivates edge servers to achieve optimal training and mining outcomes. In this paper, we investigate the incentive mechanism design for a semi-asynchronous blockchain-based federated edge learning system. We model the resource pricing mechanism among edge servers and task publishers as a Stackelberg game and prove the existence and uniqueness of a Nash equilibrium in such a game. We then propose an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) to achieve the optimal strategies for each participating edge server. Finally, our simulation results verify the convergence and efficiency of our proposed scheme.
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- Award ID(s):
- 2128378
- PAR ID:
- 10658461
- Publisher / Repository:
- ITU Journal on Future and Evolving Technologies
- Date Published:
- Journal Name:
- ITU Journal on Future and Evolving Technologies
- Volume:
- 6
- Issue:
- 2
- ISSN:
- 2616-8375
- Page Range / eLocation ID:
- 119 to 131
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.52953/XFJH6676
