Federated Learning on Non-IID Data Silos: An Experimental Study (2102.02079v4)

Abstract: Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple "data silos" (e.g., within different organizations and countries). To develop effective machine learning services, there is a must to exploit data from such distributed databases without exchanging the raw data. Recently, federated learning (FL) has been a solution with growing interests, which enables multiple parties to collaboratively train a machine learning model without exchanging their local data. A key and common challenge on distributed databases is the heterogeneity of the data distribution among the parties. The data of different parties are usually non-independently and identically distributed (i.e., non-IID). There have been many FL algorithms to address the learning effectiveness under non-IID data settings. However, there lacks an experimental study on systematically understanding their advantages and disadvantages, as previous studies have very rigid data partitioning strategies among parties, which are hardly representative and thorough. In this paper, to help researchers better understand and study the non-IID data setting in federated learning, we propose comprehensive data partitioning strategies to cover the typical non-IID data cases. Moreover, we conduct extensive experiments to evaluate state-of-the-art FL algorithms. We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing state-of-the-art FL algorithms outperforms others in all cases. Our experiments provide insights for future studies of addressing the challenges in "data silos".

Federated Learning on Non-IID Data Silos: An Experimental Study

The paper "Federated Learning on Non-IID Data Silos: An Experimental Study" by Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He addresses a critical issue in the field of federated learning (FL) - the challenge posed by non-IID (not independently and identically distributed) data across distributed databases or "data silos." This paper explores the problem of data heterogeneity, which is a common scenario in real-world applications where data is partitioned across different entities such as organizations or countries, often subject to stringent privacy and data protection regulations.

Key Contributions

1. Comprehensive Data Partitioning Strategies: The authors introduce extensive data partitioning strategies to simulate various non-IID settings. This comprehensive approach is intended to represent real-world scenarios with greater accuracy compared to previous studies, which often employed rigid and non-representative partitioning methods. This advancement paves the way for more thorough and nuanced evaluations of FL algorithms.
2. Extensive Evaluation of FL Algorithms: The paper evaluates multiple state-of-the-art FL algorithms under the proposed non-IID data settings. This empirical assessment is crucial as non-IID data poses significant challenges in retaining model accuracy and convergence efficiency in federated learning contexts. The finding that no single FL algorithm consistently outperforms the others across all non-IID scenarios underscores the complexity of the problem.

Experimental Results

The experimental results highlight significant challenges:

• Non-IID data drastically impacts the learning accuracy of FL algorithms.
• Different FL algorithms exhibit varying degrees of sensitivity to data heterogeneity.
• There is no universal FL solution that performs optimally across all non-IID settings.

Implications for Future Research

The insights from this paper have several implications:

• The need for developing more robust FL algorithms that can adaptively handle varying degrees of data heterogeneity.
• Importance of creating benchmark datasets and partitioning strategies that reflect real-world non-IID conditions, facilitating more relevant and effective evaluations.
• Future FL research should consider adaptive mechanisms that can dynamically recognize and mitigate the impacts of non-IID data distribution.

Practical Applications

Practically, this research is vital for deploying FL in scenarios where data privacy and regulation are paramount:

• Healthcare: Collaborations between hospitals can benefit from FL to train models on patient data without compromising privacy.
• Finance: Financial institutions can jointly develop fraud detection systems without sharing sensitive customer data.
• Cross-border regulations: Organizations operating in different countries can collaborate while adhering to local data protection laws.

Conclusion

This paper makes a significant contribution by providing an experimental paper that systematically explores the challenges and effectiveness of FL algorithms under non-IID data settings. The comprehensive data partitioning strategies and the thorough evaluation framework it introduces will serve as an essential reference for future research. As federated learning continues to gain traction, addressing the challenges of data heterogeneity will be crucial for achieving its full potential in real-world applications.