ASYN2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation

核心概念

The author presents ASYN2F, an asynchronous federated learning framework with bidirectional model aggregation, addressing the challenges of heterogeneity in training workers and improving performance through innovative aggregation algorithms.

要約

ASYN2F introduces a novel approach to federated learning by allowing asynchronous updates of the global model and local models, resulting in improved performance. The framework addresses practical implementation requirements such as communication protocols and storage services. Extensive experiments demonstrate the effectiveness and scalability of ASYN2F compared to state-of-the-art techniques.
The proliferation of IoT devices has led to a massive collection of data, prompting the need for efficient representation and generalization methods.
Federated learning systems consist of a server and distributed training workers, focusing on optimizing global model performance while reducing communication costs.
Existing works in federated learning mainly focus on server-side aggregation methods, overlooking practical implementation challenges related to data privacy and security policies.
ASYN2F introduces bidirectional model aggregation, allowing for asynchronous updates between the server and workers during training epochs.
The framework leverages advanced message queuing protocols for efficient communication and cloud storage for model management.
Experimental results show that ASYN2F outperforms existing techniques in terms of model performance, practicality, and scalability.

統計

Models trained by ASYN2F achieve higher performance compared to state-of-the-art techniques.
The accuracy of ResNet18 models trained by ASYN2F is significantly better than other existing techniques.

引用

"We design and develop ASYN2F, an asynchronous federated learning framework with bidirectional model aggregation."
"Extensive experiments show that models trained by ASYN2F achieve higher performance compared to state-of-the-art techniques."

抽出されたキーインサイト

Asyn2F

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

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

深掘り質問

How does ASYN2F address the issue of obsolete information at slow training workers

ASYN2F addresses the issue of obsolete information at slow training workers by implementing bidirectional model aggregation. This approach allows the server to asynchronously aggregate multiple local models and update the global model without waiting for all training workers to submit their local models. When a worker completes its training epoch, it can immediately start a new epoch with its current local model while incorporating the latest version of the global model into its training process. This bidirectional model aggregation ensures that slow training workers do not fall behind in updating their models with the most recent information from faster workers, thus reducing the impact of obsolete information.

What are the implications of bidirectional model aggregation on communication costs in federated learning frameworks

Bidirectional model aggregation in ASYN2F has implications on communication costs in federated learning frameworks by optimizing data exchange between the server and workers. By allowing both asynchronous updates from individual workers to the server and vice versa, ASYN2F minimizes unnecessary delays in aggregating local models into the global model. This reduces idle time for fast workers waiting on slower ones and enables more efficient utilization of computing resources across all participants. As a result, bidirectional model aggregation helps streamline communication processes, leading to lower overall communication costs in federated learning frameworks.

How can the concept of asynchronous updates be applied to other machine learning paradigms beyond federated learning

The concept of asynchronous updates implemented in ASYN2F can be applied to other machine learning paradigms beyond federated learning to enhance efficiency and performance. For example:

In distributed machine learning systems: Asynchronous updates can improve convergence speed by allowing individual nodes or devices within a network to update their models independently before synchronizing with a central server.
Reinforcement Learning: Agents interacting with an environment can benefit from asynchronous updates by continuously updating their policies based on experiences without needing synchronous coordination.
Transfer Learning: Asynchronous updates can facilitate knowledge transfer between different tasks or domains by enabling continuous adaptation of shared features or parameters without strict synchronization requirements.
By leveraging asynchronous updates, these machine learning paradigms can achieve better scalability, faster convergence, and improved resource utilization across distributed systems or complex environments.