Federated Online Model Selection with Decentralized Data: Balancing Collaboration, Computation, and Communication

核心概念

Collaboration is necessary for online model selection with decentralized data if the computational cost on each client is limited, but unnecessary if the computational cost is not constrained.

要約

The content discusses the problem of online model selection (OMS) with decentralized data (OMS-DecD) over multiple clients. The key insights are:

1. Collaboration is unnecessary if the computational cost on each client is allowed to be O(K), where K is the number of candidate hypothesis spaces. In this case, a non-cooperative algorithm that independently runs OMS on each client can achieve similar performance as a federated algorithm.

2. Collaboration is necessary if the computational cost on each client is limited to o(K). In this case, a federated algorithm can outperform the non-cooperative approach by leveraging collaboration among clients.

3. The authors propose two federated algorithms, FOMD-OMS (R = T) and FOMD-OMS (R < T), that balance the trade-off between prediction performance and communication cost. FOMD-OMS (R = T) achieves the optimal regret bound without communication constraint, while FOMD-OMS (R < T) explicitly controls the communication rounds.

4. As a byproduct, the authors improve the regret bounds of existing algorithms for distributed online multi-kernel learning (OMKL) at a smaller computational and communication cost.

統計

The optimal regularization parameter Ui* corresponds to the optimal hypothesis space Fi*.
The complexity of Fi is measured by Ci = Θ(UiGi + Ci), where Gi depends on the feature mapping φi.

引用

"Collaboration is unnecessary if we do not limit the computational cost on each client."
"Collaboration is necessary if we limit the computational cost on each client to o(K)."
"Our algorithms rely on three new techniques, i.e., an improved Bernstein's inequality for martingale, a federated algorithmic framework, named FOMD-No-LU, and decoupling model selection and predictions, which might be of independent interest."

抽出されたキーインサイト

On the Necessity of Collaboration in Online Model Selection with Decentralized Data

by Junfan Li,Ze... 場所 arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09494.pdf

深掘り質問

What are the potential applications of the proposed federated online model selection framework beyond the online supervised learning setting

The proposed federated online model selection framework has potential applications beyond online supervised learning. One such application is in the field of online hyper-parameter tuning, where the framework can be utilized to dynamically select the optimal regularization parameters for machine learning models. This can lead to improved model performance and efficiency in various applications such as image recognition, natural language processing, and recommendation systems. Additionally, the framework can be applied to online kernel selection tasks, where the goal is to select the most suitable kernel function for kernel-based machine learning algorithms. By leveraging the federated approach, the framework can handle decentralized data sources and facilitate collaborative model selection across multiple clients or devices.

How can the federated algorithms be extended to handle non-convex loss functions or more general hypothesis spaces beyond reproducing kernel Hilbert spaces

To extend the federated algorithms to handle non-convex loss functions or more general hypothesis spaces beyond reproducing kernel Hilbert spaces, several modifications and enhancements can be implemented. One approach is to incorporate techniques from stochastic optimization and non-convex optimization to adapt the algorithms for handling non-convex loss functions. This may involve using advanced optimization methods such as stochastic gradient descent with adaptive learning rates or incorporating regularization techniques to handle the complexity of the hypothesis spaces. Additionally, the framework can be extended to support more general hypothesis spaces by incorporating flexible model parameterizations and adaptive model selection strategies that can accommodate a wider range of model complexities and structures.

How can the trade-off between communication cost and prediction performance be further optimized in the federated setting, e.g., by adaptively adjusting the communication rounds based on the data distribution or model complexity

To optimize the trade-off between communication cost and prediction performance in the federated setting, adaptive strategies can be implemented to adjust the communication rounds based on the data distribution or model complexity. One approach is to dynamically adjust the frequency of communication rounds based on the data variability or the convergence of the models across different clients. By monitoring the model performance and communication overhead during the training process, the framework can intelligently adapt the communication schedule to optimize the overall performance. Additionally, incorporating techniques such as model compression, differential privacy, or federated learning strategies can further enhance the trade-off between communication cost and prediction accuracy in federated online model selection.