Decentralised Federated Learning: Initialisation and Topology Effects

核心概念

Decentralised federated learning's effectiveness is influenced by network topology, requiring an improved initialisation strategy for artificial neural networks.

摘要

1. Abstract
   • Decentralised federated learning enhances data privacy and eliminates central coordination.
   • Network topology significantly impacts its effectiveness.
2. Introduction
   • Traditional centralized machine learning faces data privacy risks and centralization overhead.
   • Federated learning proposes a decentralized approach.
3. Motivation
   • Decentralised federated learning introduces new challenges in initialization and network structure understanding.
4. Contribution
   • Proposes an uncoordinated neural network initialization method for deep neural networks in a decentralised setting.
5. Related Works
   • Explores the development of decentralised federated learning from previous methods.
6. Preliminaries
   • Describes the setup for decentralised federated learning with iterative processes and local training on nodes' private data.
7. Uncoordinated Artificial Neural Network Initialization
   • Introduces a method to address subpar performance in deep neural networks due to uncoordinated initialization in decentralised settings.
8. Scalability and Parameters Analysis
   • Analyzes the impact of network density, training samples per node, system size, and communication frequency on the trajectory of decentralised federated learning.

統計資料

"Our experiments will be performed on subsets of the MNIST digit classification task (LeCun et al., 1998)"
"The advent of much deeper architectures led to a methodical understanding of the role of initial parameters"
"The standard deviation across parameters at round t can be approximated by limt→∞σap ≈σinit ∥vsteady∥"

引述

"Decentralized federated learning aims to provide an alternative approach that maintains data privacy but removes the need for a central server."
"Understanding the combination and interactions of these properties adds another layer of complexity to the problem."

從以下內容提煉的關鍵洞見

Initialisation and Topology Effects in Decentralised Federated Learning

by Aras... 於 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15855.pdf

深入探究

How does decentralized federated learning compare to other approaches in terms of scalability?

Decentralized federated learning offers significant advantages in terms of scalability compared to traditional centralized approaches. In decentralized federated learning, individual nodes update their local models using local data and communicate with each other through a network, eliminating the need for a central server. This distributed approach allows for collaborative training on a large scale without the bottleneck of a single point of coordination or failure. Additionally, the use of uncoordinated initializations and communication networks can enhance privacy and efficiency.

What are potential implications of not considering non-iid labels or unequal allocation of samples in decentralized federated learning?

Not considering non-iid labels or unequal allocation of samples in decentralized federated learning can have several implications. Firstly, it may lead to biased models as different nodes may have access to different types or amounts of data, affecting the overall model performance. Secondly, ignoring these factors could result in suboptimal training processes where certain nodes contribute more than others due to imbalanced data distribution. Lastly, overlooking these aspects may hinder the generalization capabilities and fairness of the trained models across all nodes.

How can advancements in heterogeneous machine learning model architectures impact decentralized federated learning?

Advancements in heterogeneous machine learning model architectures can have a profound impact on decentralized federated learning. By allowing nodes to utilize diverse model structures tailored to their specific tasks or capabilities, this approach enables more efficient utilization of resources and expertise within a network. Heterogeneous architectures also promote specialization among nodes based on their strengths, leading to improved overall system performance and adaptability. Furthermore, such advancements facilitate complex tasks that require specialized models while maintaining data privacy and decentralization principles inherent in federated learning setups.