Información - Wireless Networks - # Adaptive Client Sampling for Federated Learning

Optimal Heterogeneous Client Sampling for Federated Learning over Wireless Networks

Q: How can the proposed adaptive client sampling scheme be extended to handle non-convex loss functions

The proposed adaptive client sampling scheme can be extended to handle non-convex loss functions by incorporating techniques that are suitable for optimizing non-convex objectives. One approach could be to use stochastic optimization methods that are robust to non-convexity, such as stochastic gradient descent with momentum or Adam optimization. These methods can help navigate the optimization landscape of non-convex functions more effectively by incorporating adaptive learning rates and momentum terms. Additionally, techniques like simulated annealing or genetic algorithms can be employed to explore the parameter space and find optimal solutions for non-convex loss functions.

Q: What are the potential drawbacks or limitations of the proposed approach, and how can they be addressed

One potential drawback of the proposed approach is the computational complexity involved in solving the optimization problem with unknown parameters, such as Gi, α, and β. To address this limitation, one possible solution is to use approximation techniques or heuristics to estimate these parameters during the learning process. Additionally, implementing more efficient algorithms for parameter estimation and optimization can help reduce the computational burden and improve the scalability of the approach. Furthermore, conducting thorough sensitivity analyses and robustness checks can help ensure the reliability and stability of the proposed method in real-world applications.

Q: How can the insights from this work on optimal client sampling be applied to other distributed learning paradigms beyond federated learning

The insights gained from the optimal client sampling approach in federated learning can be applied to other distributed learning paradigms to improve training efficiency and convergence speed. For example, in collaborative learning settings where multiple parties contribute data for model training, optimizing client sampling based on system and statistical heterogeneity can help enhance the overall learning process. Additionally, in decentralized machine learning frameworks like multi-party computation or edge computing, the principles of adaptive client sampling can be leveraged to minimize communication overhead and computational costs while ensuring convergence to desired accuracy levels. By tailoring the client sampling strategy to the specific characteristics of different distributed learning scenarios, the benefits of faster convergence and reduced training time can be realized across a variety of applications.

Conceptos Básicos

The authors design an adaptive client sampling algorithm for federated learning over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time.

Resumen

The paper aims to design an optimal client sampling strategy for federated learning (FL) over wireless networks that addresses both system heterogeneity (diverse computation and communication capacities) and statistical heterogeneity (unbalanced and non-i.i.d. data) to minimize the wall-clock convergence time.

The key contributions are:

The authors obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probability. This enables them to analytically establish the relationship between the total learning time and sampling probability.
Based on the convergence bound, the authors formulate a non-convex optimization problem to find the optimal client sampling probability. They design an efficient algorithm to approximately solve this non-convex problem.
The solution reveals the impact of system and statistical heterogeneity parameters on the optimal client sampling design. It shows that as the number of sampled clients increases, the total convergence time first decreases and then increases.
Experimental results from both hardware prototype and simulation demonstrate that the proposed sampling scheme significantly reduces the convergence time compared to baseline sampling schemes. For the EMNIST dataset, the proposed scheme in hardware prototype spends 71% less time than the baseline uniform sampling for reaching the same target loss.

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Estadísticas

The number of clients N.
The total system bandwidth ftot.
The computation time τi and communication time ti for each client i.
The data size weight pi and the expected norm of client i's stochastic gradient bound Gi for each client i.

Citas

"Federated learning (FL) has recently emerged as an attractive distributed machine learning (DML) paradigm, which enables many clients to collaboratively train a model under the coordination of a central server while keeping the training data decentralized and private."
"Recent works have provided theoretical convergence analysis that demonstrates the effectiveness of FL with partial participation in various non-i.i.d. settings. However, these prior works have focused on sampling schemes that are uniformly at random or proportional to the clients' data sizes, which often suffer from slow error convergence with respect to wall-clock (physical) time due to high degrees of the system and statistical heterogeneity."

Ideas clave extraídas de

Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks

by Bing Luo,Wen... a las arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13804.pdf

Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks

Consultas más profundas

How can the proposed adaptive client sampling scheme be extended to handle non-convex loss functions

The proposed adaptive client sampling scheme can be extended to handle non-convex loss functions by incorporating techniques that are suitable for optimizing non-convex objectives. One approach could be to use stochastic optimization methods that are robust to non-convexity, such as stochastic gradient descent with momentum or Adam optimization. These methods can help navigate the optimization landscape of non-convex functions more effectively by incorporating adaptive learning rates and momentum terms. Additionally, techniques like simulated annealing or genetic algorithms can be employed to explore the parameter space and find optimal solutions for non-convex loss functions.

What are the potential drawbacks or limitations of the proposed approach, and how can they be addressed

One potential drawback of the proposed approach is the computational complexity involved in solving the optimization problem with unknown parameters, such as Gi, α, and β. To address this limitation, one possible solution is to use approximation techniques or heuristics to estimate these parameters during the learning process. Additionally, implementing more efficient algorithms for parameter estimation and optimization can help reduce the computational burden and improve the scalability of the approach. Furthermore, conducting thorough sensitivity analyses and robustness checks can help ensure the reliability and stability of the proposed method in real-world applications.

How can the insights from this work on optimal client sampling be applied to other distributed learning paradigms beyond federated learning

The insights gained from the optimal client sampling approach in federated learning can be applied to other distributed learning paradigms to improve training efficiency and convergence speed. For example, in collaborative learning settings where multiple parties contribute data for model training, optimizing client sampling based on system and statistical heterogeneity can help enhance the overall learning process. Additionally, in decentralized machine learning frameworks like multi-party computation or edge computing, the principles of adaptive client sampling can be leveraged to minimize communication overhead and computational costs while ensuring convergence to desired accuracy levels. By tailoring the client sampling strategy to the specific characteristics of different distributed learning scenarios, the benefits of faster convergence and reduced training time can be realized across a variety of applications.