Enhancing Efficiency in Federated Learning for Constrained Devices through Selective Data Training

Core Concepts

Centaur, an end-to-end federated learning framework, enhances efficiency in multidevice federated learning by incorporating on-device data selection and partition-based model training to address the resource constraints of ubiquitous constrained devices.

Abstract

The content discusses the challenges of deploying federated learning (FL) on ubiquitous constrained devices (UCDs) and proposes Centaur, an end-to-end FL framework to address these challenges.
Key highlights:

UCDs have limited memory, computing, and connectivity resources, making it difficult to train deep neural networks (DNNs) locally.
Centaur partitions the DNN into an encoder (feature extractor) and a lightweight classifier. The encoder is trained on more powerful access points (APs), while the classifier is trained on UCDs.
Centaur performs dynamic data selection on UCDs based on loss values and gradient norms to decide which data samples to discard, train locally on the classifier, or transmit to the AP for training the full model.
Evaluations on benchmark datasets and models show that Centaur achieves 19% higher accuracy and 58% lower latency compared to baseline FL without the proposed strategies.
Centaur effectively handles challenges like imbalanced data, client participation heterogeneity, and device mobility patterns.

Stats

On average, Centaur achieves 19% higher accuracy and 58% lower latency compared to baseline FL without the proposed strategies.
Centaur reduces the computational workload on UCDs by 58% and on APs by 59% compared to baseline approaches.
Centaur reduces the communication workload on UCDs by 67% and on APs by 30% compared to baseline approaches.

Quotes

"Centaur, through data selection and partition-based model training, achieves up to 19% higher accuracy and 58% lower latency, on average."
"Our experiments on a small testbed of RaspberryPis validate that Centaur is promising in decreasing overall storage requirements and training time due to employing effective data selection."

Key Insights Distilled From

Enhancing Efficiency in Multidevice Federated Learning through Data Selection

by Fan Mo,Moham... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2211.04175.pdf

Deeper Inquiries

How can Centaur's data selection and partition-based training be extended to handle more complex model architectures and diverse device capabilities

Centaur's data selection and partition-based training can be extended to handle more complex model architectures and diverse device capabilities by implementing adaptive strategies. For more complex model architectures, the data selection process can be enhanced to consider additional factors such as layer-wise importance, activation patterns, or gradient flow. This would involve analyzing the impact of each data sample on different layers of the model and adjusting the selection criteria accordingly. Additionally, for diverse device capabilities, the partition-based training can be customized based on the resources available on each device. This could involve dynamically allocating tasks to devices based on their processing power, memory capacity, and connectivity strength. By incorporating adaptive mechanisms, Centaur can effectively handle a wider range of model architectures and device capabilities in a flexible and efficient manner.

What are the potential privacy and security implications of the data selection and transmission process in Centaur, and how can they be addressed

The data selection and transmission process in Centaur may raise privacy and security concerns due to the potential exposure of sensitive information during the training process. To address these implications, several measures can be implemented:

Differential Privacy: Introduce differential privacy techniques to ensure that individual data samples cannot be reverse-engineered from the model updates.
Secure Aggregation: Implement secure aggregation protocols to protect the privacy of individual device contributions during the model aggregation phase.
Encryption: Encrypt the data transmission between devices to prevent unauthorized access to the information being shared.
Data Anonymization: Anonymize the data samples before transmission to remove any personally identifiable information.
Access Control: Implement strict access control mechanisms to regulate the data access and sharing permissions among devices.
By incorporating these privacy and security measures, Centaur can mitigate the risks associated with data selection and transmission in federated learning.

How can the insights from Centaur be applied to improve the efficiency of federated learning in other domains beyond constrained devices, such as edge computing or Internet of Things applications

The insights from Centaur can be applied to improve the efficiency of federated learning in other domains beyond constrained devices, such as edge computing or Internet of Things (IoT) applications. Some potential applications include:

Edge Computing: Centaur's approach to data selection and partition-based training can be utilized in edge computing environments to optimize model training on edge devices with limited resources. By adapting the framework to edge computing settings, it can enhance the efficiency of distributed learning tasks across edge nodes.
IoT Applications: In IoT scenarios where devices generate vast amounts of data, Centaur's strategies can be leveraged to enable collaborative learning while preserving data privacy. By extending the framework to IoT networks, it can facilitate efficient model training across a network of interconnected devices.
Healthcare and Smart Cities: Centaur's techniques can be applied in healthcare settings and smart city applications to enable collaborative learning from distributed data sources while ensuring data security and privacy. By tailoring the framework to these domains, it can support the development of intelligent systems that learn from diverse data sources in a privacy-preserving manner.
By adapting Centaur's principles to these domains, federated learning can be enhanced to address the unique challenges and requirements of edge computing and IoT applications, leading to more efficient and secure collaborative learning processes.