Federated Machine Learning: Concept and Applications (1902.04885v1)

Abstract: Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.

• The paper defines federated learning paradigms that enable decentralized training without centralizing sensitive data.
• It details secure methodologies like multi-party computation, differential privacy, and homomorphic encryption to protect information during model aggregation.
• The study demonstrates that federated learning maintains competitive accuracy compared to centralized models across applications in healthcare, finance, and smart retail.

Federated Machine Learning: Concept and Applications

Authors: Qiang Yang (Hong Kong University of Science and Technology, Hong Kong), Yang Liu (Webank, China), Tianjian Chen (Webank, China), Yongxin Tong (Beihang University, China)

Overview

The paper "Federated Machine Learning: Concept and Applications" addresses two significant challenges in the domain of AI: the isolation of valuable data across different entities, and the critical need to enhance data privacy and security. The authors propose federated learning (FL) as a robust framework to enable decentralized learning from data across multiple institutions without necessitating data centralization. The paper provides a comprehensive set of definitions, architectures, applications, and security mechanisms for federated learning, including a thorough review of existing research efforts.

Key Contributions

The critical components of the federated learning framework are discussed in-depth, including:

1. Federated Learning Paradigms: The authors delineate three main categories of federated learning:
   • Horizontal Federated Learning: Suitable for scenarios where datasets share the same feature space but differ in samples.
   • Vertical Federated Learning: Applicable when datasets share the same sample space but differ in feature space.
   • Federated Transfer Learning: Combines elements of both horizontal and vertical learning to address scenarios where datasets differ in both samples and features.
2. Security and Privacy Mechanisms: Several methodologies are discussed to ensure data privacy and security throughout the federated learning process:
   • Secure Multi-party Computation (SMC): Provides a mechanism for zero-knowledge proofs.
   • Differential Privacy: Adds noise or uses generalization methods to protect individual data records.
   • Homomorphic Encryption: Enables computation on encrypted data without decrypting it, thus preventing data leakage during analysis.
3. Architectural Models: The paper outlines detailed workflow and system architectures for both horizontal and vertical federated learning systems:
   • In horizontal federated learning, the process involves secure aggregation of local models trained on individual devices.
   • In vertical federated learning, the workflow requires encrypted intermediate results to be shared between collaborating parties to compute gradients and losses without exposing private data.
4. Security Definitions: Different security assumptions are made for horizontal and vertical federated learning models, explaining how these frameworks prevent leakage of sensitive information even under semi-honest conditions.

Results and Implications

The paper posits federated learning as a transformative tool for industries facing data isolation and privacy issues:

• Performance Metrics: Federated learning maintains model performance (δ-accuracy loss) comparable to centralized models while preserving data privacy.
• Data Integration: The framework allows for the integration of heterogeneous data across different entities without the need for data migration or exposure, adhering to regulations such as GDPR.

Practical Applications

Federated learning has immense potential for applications across various industries:

• Smart Retail: By federating data from banks, social networks, and e-commerce platforms, personalized customer services can be provided without breaching data privacy.
• Financial Sector: In multi-party financial scenarios, federated learning can aid in detecting fraud without exposing user data across different institutions.
• Healthcare: Medical institutions can leverage federated learning to build comprehensive predictive models, enhancing diagnostic accuracy without violating patient confidentiality.

Future Directions

The paper suggests numerous avenues for future research and industrial applications in federated learning:

1. Incentive Mechanisms: Developing fair and transparent incentive structures to encourage data sharing among organizations.
2. Improved Security Protocols: Further advancing the security mechanisms to counter potential adversarial threats in federated learning.
3. Scalability and Efficiency: Enhancing the scalability and efficiency of federated learning systems to handle large-scale deployments.

Federated learning represents a significant step forward in achieving secure, collaborative, and effective AI models across diverse sectors. The framework not only addresses current challenges in isolated data and privacy but also sets the groundwork for future advancements in data-driven AI applications.