Addressing Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

Core Concepts

The author presents a novel Federated Semi-supervised Learning framework, FedDure, to tackle data imbalances by introducing dual regulators. The approach involves a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg) to adaptively update the local model based on unique client data distributions.

Abstract

The content introduces FedDure, a novel framework for Federated Semi-supervised Learning that addresses data imbalances across clients and within each client. By utilizing dual regulators, C-reg and F-reg, the model optimizes the local training process to improve performance significantly compared to existing methods. The paper provides theoretical analysis of convergence and empirical evidence showcasing superior results on various datasets.
Existing Federated Learning methods assume fully labeled private data in clients, but this is unrealistic in real-world scenarios due to annotation challenges. Federated Semi-supervised Learning (FSSL) aims to enhance model performance with limited labeled and abundant unlabeled data on each client. The proposed FedDure framework introduces dual regulators, C-reg and F-reg, to dynamically adjust gradient updates based on class distribution characteristics within and across clients.
FedDure's innovative approach involves C-reg regulating the importance of local training on unlabeled data by quantifying learning effects using labeled data feedback. Meanwhile, F-reg learns an adaptive weighting scheme for each client's unlabeled instances to address internal imbalance effectively. Through bi-level optimization, FedDure demonstrates superior performance over existing methods across multiple benchmarks under internal and external imbalances.
The experiments conducted showcase FedDure's effectiveness in improving performance under different levels of data heterogeneity settings. Ablation studies confirm the significance of both components - C-reg and F-reg - in enhancing Federated Semi-supervised Learning outcomes. Additionally, the method proves robust against varying percentages of labeled instances per client and selected clients per round.
Further research directions could explore the scalability of FedDure to larger datasets or investigate potential privacy implications associated with federated learning approaches.

Stats

Existing FSSL methods perform worse than training with only a small portion of labeled data.
FedDure improves accuracy by 12.17% on CIFAR10 and by 11.16% on CINIC-10 datasets.
The proposed framework utilizes dual regulators - C-reg and F-reg - for adaptive model updates.
Performance comparisons demonstrate FedDure's superiority over state-of-the-art methods across various datasets.
Ablation studies highlight the importance of both components - C-reg and F-reg - in enhancing FSSL outcomes.

Quotes

"FedDure explores two adaptive regulators, a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg), to flexibly update the local model according to the unique learning processes."
"We propose FedDure, a new FSSL framework that designs dual regulators to adaptively update the local model according to the unique learning processes."
"Our work primarily focuses on federated semi-supervised learning, where a small fraction of data has labels in each client."

Key Insights Distilled From

Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

by Sikai Bai,Sh... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2307.05358.pdf

Deeper Inquiries

How can FedDure be adapted for use in other machine learning domains beyond semi-supervised learning

FedDure can be adapted for use in other machine learning domains beyond semi-supervised learning by modifying the components and mechanisms to suit the specific requirements of different domains. For example:

In supervised learning, FedDure can be adjusted to focus on optimizing model performance with limited labeled data across decentralized clients.
In reinforcement learning, FedDure could be tailored to incorporate dual regulators that adaptively adjust policy updates based on feedback from each client's environment.
In unsupervised learning, FedDure may need modifications to handle clustering or generative tasks where there is no labeled data available.
By customizing the regulators and optimization processes, FedDure can potentially enhance model training in various machine learning domains where decentralized data sources are present.

What are potential drawbacks or limitations of relying heavily on unlabeled data in federated learning scenarios

Relying heavily on unlabeled data in federated learning scenarios can have several drawbacks or limitations:

Quality of Pseudo Labels: The accuracy of pseudo labels generated from unlabeled data may not always be reliable, leading to noisy supervision signals that could degrade model performance.
Data Distribution Mismatch: Unlabeled data might have a different distribution compared to labeled data within a client or across clients, causing challenges in generalization and convergence during training.
Increased Communication Overhead: Utilizing large amounts of unlabeled data requires more communication between clients and the central server, which can lead to higher bandwidth usage and slower convergence rates.
Privacy Concerns: Aggregating information from diverse unlabeled datasets raises privacy concerns as sensitive information might inadvertently leak during the federated learning process.

Balancing the utilization of labeled and unlabeled data effectively while considering these limitations is crucial for successful federated semi-supervised learning.

How might advancements in federated learning impact broader applications such as healthcare or consumer products

Advancements in federated learning have significant implications for broader applications such as healthcare or consumer products:

Healthcare: Federated Learning enables collaborative model training without sharing patient-specific data, ensuring privacy compliance while improving medical image analysis accuracy or disease diagnosis through collective intelligence.
Consumer Products: By leveraging Federated Learning techniques, companies can enhance personalized recommendations without compromising user privacy by aggregating insights from multiple devices securely.

These advancements pave the way for innovative solutions that prioritize both privacy protection and enhanced model performance across various sectors like healthcare diagnostics or personalized product recommendations in consumer markets.