Effective Knowledge Fusion in Federated Learning: KnFu Algorithm

핵심 개념

The KnFu algorithm evaluates and fuses effective knowledge among clients in Federated Learning to prevent model drift.

초록

The paper introduces the KnFu algorithm, addressing challenges in Federated Learning (FL) by selectively fusing relevant knowledge among clients. FL decentralizes training across nodes, but faces issues like model drift due to non-IID datasets. The proposed algorithm evaluates each client's knowledge for others, preventing adverse impacts on local models. By strategically fusing semantic neighbors' effective knowledge, KnFu offers personalized solutions. Experiments on MNIST and CIFAR10 datasets show KnFu's effectiveness over existing methods. The research emphasizes personalized knowledge fusion's potential in managing FL complexities.

통계

"Comprehensive experiments were performed on MNIST and CIFAR10 datasets illustrating effectiveness of the proposed KnFu"
"A key conclusion of the work is that in scenarios with large and highly heterogeneous local datasets, local training could be preferable to knowledge fusion-based solutions."

인용구

"Not all knowledge is universally beneficial due to the inherent diversity of data among local nodes."
"The KnFu is a personalized effective knowledge fusion scheme for each client."
"In scenarios with large and highly heterogeneous local datasets, local training could be preferable to knowledge fusion-based solutions."

핵심 통찰 요약

KnFu

by S. Jamal Sey... 게시일 arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11892.pdf

더 깊은 질문

How can selective knowledge fusion impact the scalability of Federated Learning

Selective knowledge fusion can have a significant impact on the scalability of Federated Learning (FL) by addressing key challenges related to model drift and data heterogeneity. By selectively fusing relevant knowledge from different clients based on their semantic similarity, the process becomes more efficient and targeted. This selective approach ensures that only beneficial knowledge is shared among clients, reducing the risk of adverse impacts on individual models. As a result, the overall FL system can scale more effectively as it optimizes the aggregation of knowledge across diverse datasets without compromising performance.

What are the potential drawbacks of relying solely on local training in FL environments

Relying solely on local training in FL environments can lead to several potential drawbacks that may hinder the effectiveness and efficiency of the learning process. One major drawback is the limited scope of learning when each client's model operates independently without leveraging insights from other models. This lack of collaboration can result in suboptimal performance due to variations in data distributions and model architectures across different nodes. Additionally, local training alone may not fully address issues such as gradient inversion attacks or communication overhead, which are common challenges in FL settings requiring collaborative approaches for mitigation.

How can the concept of personalized knowledge fusion be applied beyond machine learning contexts

The concept of personalized knowledge fusion extends beyond machine learning contexts and holds relevance in various domains where heterogeneous data sources need to be integrated effectively. For example:

Healthcare: Personalized treatment plans could benefit from integrating patient data while ensuring privacy and security.
Finance: Tailored financial recommendations could be generated by fusing relevant information from diverse portfolios.
Supply Chain Management: Optimizing supply chain operations by aggregating insights from various stakeholders while preserving sensitive business information.
By applying personalized knowledge fusion techniques outside ML contexts, organizations can enhance decision-making processes, improve collaboration among disparate entities, and drive innovation through effective integration of diverse datasets.