spostrzeżenie - Machine Learning - # Federated Learning Framework

ASYN2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation

Q: How does ASYN2F handle the heterogeneity of training workers

ASYN2F handles the heterogeneity of training workers by allowing for asynchronous model aggregation and bidirectional communication between the server and the workers. This approach enables the server to asynchronously aggregate multiple local models without waiting for all training workers to submit their models. Additionally, ASYN2F allows each worker to incorporate the new version of the global model into their local model during training, reducing delays caused by slower workers. By considering factors such as data quality, dataset size, and loss value in model aggregation algorithms, ASYN2F effectively addresses the challenges posed by heterogeneous training resources.

Q: What are the implications of faster convergence in federated learning frameworks like ASYN2F

The implications of faster convergence in federated learning frameworks like ASYN2F are significant. Faster convergence means that models can reach desired performance levels more quickly, leading to reduced training costs in terms of time and computational resources. With faster convergence rates, users can stop training when a satisfactory level of performance is achieved, optimizing resource utilization and potentially lowering overall costs associated with model development. Additionally, faster convergence enhances real-time decision-making capabilities as models can be deployed sooner for practical applications.

Q: How can bidirectional model aggregation benefit other machine learning applications beyond federated learning

Bidirectional model aggregation implemented in ASYN2F offers benefits beyond federated learning applications. In other machine learning contexts where distributed or collaborative learning is utilized, bidirectional aggregation can enhance model updating processes across different nodes or devices. For example: Decentralized Edge Computing: Bidirectional aggregation can facilitate efficient collaboration among edge devices with varying computing capabilities. Collaborative Learning Environments: In educational settings where multiple learners contribute to a shared model (e.g., group projects), bidirectional aggregation ensures timely updates from individual contributions. Distributed Sensor Networks: In IoT systems with sensor networks collecting data from various sources, bidirectional aggregation helps maintain up-to-date global models while accommodating diverse data streams. By enabling seamless integration of updated information from multiple sources into a central model iteratively and efficiently bidirectionally aggregated results benefit various machine learning applications beyond federated learning scenarios.

Główne pojęcia

ASYN2F is an effective asynchronous federated learning framework that outperforms existing techniques in terms of performance and convergence speed.

Streszczenie

ASYN2F introduces bidirectional model aggregation, allowing for faster convergence and lower communication costs compared to other methods. The framework addresses the issue of obsolete information at workers, leading to improved model performance. Extensive experiments demonstrate the superiority of ASYN2F in various scenarios, making it a practical solution for real-world deployment.

Dostosuj podsumowanie

Przepisz z AI

Generuj cytaty

Przetłumacz źródło

Na inny język

Generuj mapę myśli

z treści źródłowej

Odwiedź źródło

arxiv.org

Statystyki

ASYN2F achieves 92.86% accuracy with fixed LR=0.01 on overlapping iid sub-datasets.
Synchronously-decayed LR setting results in 95.48% accuracy for ASYN2F on overlapping iid sub-datasets.
ASYN2F converges faster than M-Step KAFL and FedAvg in all experimental scenarios.

Cytaty

Kluczowe wnioski z

Asyn2F

by Tien-Dung Ca... o arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01417.pdf

Głębsze pytania

How does ASYN2F handle the heterogeneity of training workers

ASYN2F handles the heterogeneity of training workers by allowing for asynchronous model aggregation and bidirectional communication between the server and the workers. This approach enables the server to asynchronously aggregate multiple local models without waiting for all training workers to submit their models. Additionally, ASYN2F allows each worker to incorporate the new version of the global model into their local model during training, reducing delays caused by slower workers. By considering factors such as data quality, dataset size, and loss value in model aggregation algorithms, ASYN2F effectively addresses the challenges posed by heterogeneous training resources.

What are the implications of faster convergence in federated learning frameworks like ASYN2F

The implications of faster convergence in federated learning frameworks like ASYN2F are significant. Faster convergence means that models can reach desired performance levels more quickly, leading to reduced training costs in terms of time and computational resources. With faster convergence rates, users can stop training when a satisfactory level of performance is achieved, optimizing resource utilization and potentially lowering overall costs associated with model development. Additionally, faster convergence enhances real-time decision-making capabilities as models can be deployed sooner for practical applications.

How can bidirectional model aggregation benefit other machine learning applications beyond federated learning

Bidirectional model aggregation implemented in ASYN2F offers benefits beyond federated learning applications. In other machine learning contexts where distributed or collaborative learning is utilized, bidirectional aggregation can enhance model updating processes across different nodes or devices. For example:

Decentralized Edge Computing: Bidirectional aggregation can facilitate efficient collaboration among edge devices with varying computing capabilities.
Collaborative Learning Environments: In educational settings where multiple learners contribute to a shared model (e.g., group projects), bidirectional aggregation ensures timely updates from individual contributions.
Distributed Sensor Networks: In IoT systems with sensor networks collecting data from various sources, bidirectional aggregation helps maintain up-to-date global models while accommodating diverse data streams.
By enabling seamless integration of updated information from multiple sources into a central model iteratively and efficiently bidirectionally aggregated results benefit various machine learning applications beyond federated learning scenarios.