Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients

Core Concepts

Selecting beneficial local gradients accelerates Federated Learning convergence.

Abstract

The content discusses the BHerd strategy for accelerating Federated Learning (FL) convergence by selecting beneficial local gradients. It addresses the challenges posed by Non-IID datasets in FL systems and proposes a method to mitigate their effects. The paper outlines experiments conducted on different datasets and models, demonstrating the effectiveness of the BHerd strategy in improving model convergence.

Introduction to Federated Learning and its challenges.
Proposal of the BHerd strategy for selecting beneficial local gradients.
Experiments conducted on various datasets and models to validate the strategy.
Comparison with baseline approaches like Centralized SGD, FedAvg, GraB-FedAvg, FedNova, and SCAFFOLD.
Sensitivity analysis of hyperparameters like alpha, epochs per round, batch size, and number of clients.
Distribution of selected local gradients and their distance from the mean gradient.

Stats

We set |D| = 6 × 10^4, B = 100, E = 1.
Total round T = 500 with learning rate η = 1 × 10^-4.

Quotes

"In pursuit of this subset, a reliable approach involves determining a measure of validity to rank the samples within the dataset."
"Our BHerd strategy is effective in selecting beneficial local gradients to mitigate the effects brought by the Non-IID dataset."

Key Insights Distilled From

Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients

by Ping Luo,Xia... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16557.pdf

Deeper Inquiries

How can the BHerd strategy be adapted for other machine learning frameworks

The BHerd strategy can be adapted for other machine learning frameworks by incorporating the concept of selecting beneficial local gradients into their optimization processes. This adaptation involves mapping the distribution of local datasets to the local gradients and using a herding strategy to select a subset of gradients that are closer to the average gradient. By implementing this approach in different machine learning frameworks, it is possible to accelerate convergence and improve model performance by focusing on the most relevant training samples.

What are potential drawbacks or limitations of focusing on selecting only beneficial local gradients

One potential drawback of focusing on selecting only beneficial local gradients is the risk of overlooking important information contained in less prioritized gradients. By narrowing down the selection to a subset deemed beneficial, there is a possibility of discarding valuable insights or patterns present in other data points. Additionally, this selective approach may introduce bias into the model if certain crucial but initially overlooked features are not considered during training. It's essential to strike a balance between pruning non-essential information and retaining diverse perspectives for robust model generalization.

How might advancements in federated learning impact broader applications beyond machine learning

Advancements in federated learning have significant implications beyond machine learning applications. The ability to train models across decentralized devices while preserving data privacy opens up opportunities for collaborative research, healthcare diagnostics, financial analysis, and more fields where sensitive data sharing is restricted. Federated learning can revolutionize industries by enabling efficient collaboration without compromising individual privacy or security concerns. Moreover, as federated learning techniques evolve and become more sophisticated, they have the potential to drive innovation in distributed computing systems and contribute towards building secure and scalable infrastructure for various sectors beyond traditional machine learning domains.