Accelerating Federated Learning with Approximated Global Hessian (FAGH)

Core Concepts

FAGH accelerates global model training in federated learning by leveraging approximated global Hessian, reducing communication rounds and training time.

Abstract

In the realm of federated learning, the FAGH method aims to address the challenge of heavy communication overhead due to slow convergence speeds. By utilizing an approximated global Hessian, FAGH accelerates the convergence of the global model training process. This method leverages the first moment of the approximated global Hessian and gradient to train the global model efficiently. Experimental results confirm that FAGH outperforms several state-of-the-art FL training methods by decreasing communication rounds and achieving predefined performance objectives for the global model. The proposed approach is beneficial for scenarios with heterogeneous data distribution, offering a practical solution for accelerating FL training while reducing computational costs and memory inefficiency.

Stats

One potential solution is to employ Newton-based optimization known for its quadratic convergence rate.
Experimental results verify FAGH's effectiveness in decreasing communication rounds and training time.
FAGH utilizes only the first row of the true Hessian when determining the Newton direction.
The server needs to retain information on previous global gradient and first row of previous global Hessian.
The overall time complexity of the server in FAGH is O(d).

Quotes

"FAGH accelerates the convergence of global model training, leading to reduced communication rounds."
"FAGH outperforms several state-of-the-art FL training methods."
"FAGH can provide faster FL training while achieving a certain precision of global model performance."

Key Insights Distilled From

FAGH

by Mrinmay Sen,... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11041.pdf

Deeper Inquiries

How can FAGH be adapted for scenarios with highly imbalanced data distributions

FAGH can be adapted for scenarios with highly imbalanced data distributions by incorporating techniques to address the challenges posed by such data settings. One approach could involve adjusting the aggregation process to give more weight or importance to clients with less representative data, ensuring their contributions are not overshadowed by those with more abundant samples. Additionally, implementing personalized federated learning strategies within FAGH could help tailor model updates based on individual client characteristics, thereby mitigating the impact of imbalanced data distributions.

What are potential drawbacks or limitations of relying on approximated Hessians in federated learning

While approximated Hessians offer computational advantages in federated learning, there are potential drawbacks and limitations to consider. One limitation is the loss of accuracy compared to using true Hessians, which may affect the convergence speed and final performance of the global model. Approximations introduce errors that can accumulate over iterations, leading to suboptimal solutions or slower convergence rates. Moreover, depending on the approximation method used, there may be challenges in balancing computational efficiency with maintaining sufficient accuracy for effective optimization.

How might advancements in compression techniques further enhance communication efficiency in federated learning systems

Advancements in compression techniques have significant potential to enhance communication efficiency in federated learning systems further. By reducing the size of transmitted data without compromising essential information integrity, compression techniques can minimize communication costs and latency during model aggregation across distributed clients. Techniques like quantization, sparsification, and differential privacy mechanisms can optimize bandwidth usage while preserving privacy and security protocols within federated learning setups. Implementing these advancements strategically can streamline communication processes and improve overall system performance significantly.