A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (1907.09693v7)

Abstract: Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To achieve smooth flow and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.

• The paper introduces a comprehensive definition and categorization of federated learning systems based on key design and privacy aspects.
• It details case studies that evaluate system efficiency, privacy-preserving techniques, and communication architectures in collaborative settings.
• The survey outlines future research directions including privacy enhancements, interoperability, and optimization strategies for scalable systems.

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

The paper "A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection" by Qinbin Li et al. offers an extensive review and analysis of federated learning systems (FLSs). As collaborative training of machine learning models continues to attract significant attention due to its potential for enhancing data privacy and protection, this survey provides an invaluable overview of the current landscape, pertinent challenges, and future research directions in federated learning systems.

Overview and Objectives

The paper aims to achieve three primary objectives:

1. Introduce the definition and core components of federated learning systems.
2. Categorize existing systems based on six distinct aspects: data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation, and motivation of federation.
3. Highlight design factors through case studies and propose future research opportunities.

Fedearated learning allows multiple organizations to collaboratively train machine learning models without centrally aggregating their data, thereby enhancing privacy-preserving capabilities. This survey focuses on the critical need for robust systems and infrastructures, akin to those in deep learning frameworks like PyTorch and TensorFlow, to support the diverse requirements of federated learning approaches.

Key Contributions

Definition and Components

The paper begins by thoroughly defining federated learning systems and detailing their core components. These include the client and server architecture, communication protocols, aggregation algorithms, and privacy-preserving techniques. The authors emphasize the significance of these components in determining the system's efficiency, effectiveness, and privacy.

System Categorization

The paper provides a comprehensive categorization of federated learning systems based on:

• Data Distribution: How data is partitioned across clients.
• Machine Learning Model: Types of models supported.
• Privacy Mechanism: Techniques employed to ensure data privacy.
• Communication Architecture: Methods for client-server and client-client interactions.
• Scale of Federation: Number of clients involved.
• Motivation of Federation: Underlying goals, such as commercial, academic, or public interest.

Design Factors and Case Studies

Utilizing the above categorization, the authors systematically summarize the extant federated learning systems, delineating the design factors that contribute to their respective operational benefits and drawbacks. The paper also includes detailed case studies that elucidate these design factors, thereby aiding future system design efforts.

Practical and Theoretical Implications

The findings and conclusions drawn from this survey have several practical and theoretical implications:

• Privacy Enhancements: The analysis of privacy mechanisms provides critical insights into balancing model performance with stringent data protection.
• System Efficiency: Identifying bottlenecks and proposing optimization strategies to enhance the efficiency of federated learning systems.
• Scalability Considerations: Addressing challenges in scaling federated learning to larger federations, which is crucial for real-world applications.

Future Research Directions

The paper concludes with a forward-looking perspective, identifying key research opportunities including:

• Improved Privacy Mechanisms: The development of novel privacy-preserving techniques that offer better trade-offs between privacy and utility.
• Interoperability: Enhancing the interoperability between different federated learning systems and integrating them with existing machine learning ecosystems.
• Optimization Strategies: Innovating new methods to optimize communication and computation overhead in federated learning processes.
• Benchmarking Standards: Establishing standardized benchmarks for evaluating federated learning systems across diverse application domains.

Conclusion

In summary, this survey serves as a pivotal reference for researchers and practitioners in the field of federated learning. By categorizing and critically analyzing existing federated learning systems, the authors offer a structured roadmap for future advancements in the domain. This work underscores the importance of continued efforts in developing scalable, efficient, and privacy-preserving federated learning infrastructures, paving the way for broader and more secure applications of collaborative machine learning.