Hierarchical Federated Learning in Wireless Networks: Pruning for Bandwidth Scarcity and Heterogeneity

Core Concepts

Pruning-enabled Hierarchical Federated Learning optimizes convergence in wireless networks.

Abstract

The content discusses Hierarchical Federated Learning (HFL) in wireless networks, focusing on model pruning to address bandwidth scarcity and system heterogeneity. The paper proposes a Pruning-enabled Hierarchical Federated Learning (PHFL) framework that optimizes convergence rates by jointly configuring wireless resources and system parameters. Through theoretical analysis and simulations, the effectiveness of PHFL is validated in terms of test accuracy, training time, energy consumption, and bandwidth requirements. Structure: Introduction to Federated Learning in Wireless Networks Proposed Pruning-enabled Hierarchical Federated Learning Framework Convergence Analysis and Optimization Strategies Simulation Results and Validation Highlights: Practical constraints in wireless networks necessitate model pruning for efficient learning. PHFL algorithm optimizes convergence by adjusting parameters under strict constraints. Extensive simulations confirm PHFL's effectiveness across various metrics.

Stats

Owing to these practical constraints and system models, this paper leverages model pruning. Through extensive simulation, we validate the effectiveness of our proposed PHFL algorithm.

Quotes

Key Insights Distilled From

Hierarchical Federated Learning in Wireless Networks

by Md Ferdous P... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2308.01562.pdf

Deeper Inquiries

How does model pruning impact the overall efficiency of federated learning systems

Model pruning impacts the overall efficiency of federated learning systems in several ways. Firstly, by reducing the size of the model through pruning, less computation power is required during training at each client device. This leads to faster training times and lower energy consumption, making the overall process more efficient. Additionally, a smaller model size results in reduced communication overhead when transmitting updates to the central server or between hierarchical levels in federated learning systems. This optimization can help alleviate bandwidth constraints and improve system performance. Moreover, model pruning can also enhance generalization by preventing overfitting on local datasets, leading to better test accuracy without sacrificing much of the original model's performance.

What are the potential drawbacks or limitations of hierarchical federated learning with model pruning

While hierarchical federated learning with model pruning offers various benefits, there are potential drawbacks and limitations to consider. One limitation is that introducing errors through model pruning may impact convergence rates and final accuracy negatively. Pruning an ML model makes it sparser, which could lead to suboptimal solutions as compared to using the full dense models for training. Additionally, managing different aggregation strategies at multiple hierarchical levels can introduce complexity into the system design and implementation process. Ensuring synchronization among these levels while incorporating model pruning techniques adds another layer of challenge.

How can the concept of hierarchical federated learning be applied to other domains beyond wireless networks

The concept of hierarchical federated learning can be applied beyond wireless networks to various other domains where distributed data sources exist with limited resources or privacy concerns. For example: Healthcare: In healthcare settings where patient data is stored across different hospitals or clinics due to privacy regulations, hierarchical federated learning can enable collaborative training of machine learning models without sharing sensitive information. Finance: Financial institutions dealing with customer data from diverse branches or regions can utilize hierarchical federated learning for fraud detection or risk assessment while maintaining data security. Smart Cities: Urban environments collecting data from IoT devices spread across different locations could benefit from hierarchical federated learning for optimizing services like traffic management or waste disposal without compromising individual user privacy. By adapting this approach in various sectors, organizations can leverage decentralized data sources efficiently while preserving confidentiality and improving overall system performance through collaborative machine learning techniques combined with model pruning strategies.