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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ADMM-Based Hessian-Free Federated Meta-Learning for Regularized Edge Learning
In order to meet the requirements for safety and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge, calling for edge intelligence. To facilitate fast edge learning, this work advocates a platform-aided federated meta-learning architecture, where a set of edge nodes joint force to learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer. The federated meta-learning problem is cast as a regularized stochastic optimization problem, using Bregman Divergence between the edge model and the cloud pre-trained model as the regularization. We then devise an alternating direction method of multiplier (ADMM) based Hessian-free federated meta-learning algorithm, called ADMM-FedMeta, with inexact Hessian estimation. Further, we analyze the convergence properties and the rapid adaptation performance of ADMM-FedMeta for the general non-convex case. The theoretical results show that under mild conditions, ADMM-FedMeta converges to an $\epsilon$-approximate first-order stationary point after at most $\mathcal{O}(1/\epsilon^2)$ communication rounds. Extensive experimental studies on benchmark datasets demonstrate the effectiveness and efficiency of ADMM-FedMeta, and showcase that ADMM-FedMeta outperforms the existing baselines.
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- Award ID(s):
- 2203238
- NSF-PAR ID:
- 10352180
- Date Published:
- Journal Name:
- MobiHoc '21: Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
- Page Range / eLocation ID:
- 91-100
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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